Systems and methods for customer value optimization involving relationship optimization

ABSTRACT

Systems and methods can provide for customer value optimization. The customer value optimization can include analyzing certain transaction and/or non-transaction data of customers with one or more predictive models to determine predictive modeling scores, values, or indicators. These one or more predictive modeling scores, values, or indicators can be used with other transaction or non-transaction data of customers, either alone or with other derived values/calculations, to provide certain optimizations relating to relationship optimization.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 12/893,841 filed Sep. 29, 2010.

FIELD OF THE INVENTION

Aspects of the invention relate generally to analyzing transaction andnon-transaction information of customers, and more particularly, tosystems and methods for customer value optimization involvingrelationship optimization.

BACKGROUND OF THE INVENTION

Many financial institutions do not have the capability to determine howto allocate their resources to improve the value of or relationship withtheir current customers. In this way, many financial institutions wouldbenefit from the ability to identify or target customers or associatedproducts/services for customers that are likely to increase customervalue or otherwise improve the customers' relationship with thefinancial institution. Accordingly, there is an opportunity for systemsand methods for customer value optimization involving relationshipoptimization.

SUMMARY OF THE INVENTION

According to an example embodiment of the invention, there is a method.The method may include receiving data associated with a customer of afinancial institution, the data associated with at least one of (i)financial transaction data of the customer, or (ii) financial accountdata of the customer; identifying one of a plurality of segments for thecustomer, wherein the identified segment is based at least in part on afirst portion of the input data; calculating a current value of thecustomer, the current value based at least in part upon one or morecurrent holdings of existing products or services by the customer withthe financial institution; calculating a future value of the customer,the future value based upon a probability of purchase for at least oneproduct or service and a measure of profitability for the at least oneproduct or service, the measure of profitability identified based atleast in part from the identified segment for the customer; anddetermining, based at least in part on a combination of the currentvalue and the future value, that the customer is eligible for at leastone recommended next action, wherein each recommended next actionincludes a new product or service for the customer or a modification toan existing product or service of the customer. One or more of the priorsteps may be performed by one or more computers.

According to another example embodiment of the invention, there is asystem. The system may include at least one memory comprisingcomputer-executable instructions; at least one communications interface;and at least one processor in communication with the at least onecommunications interface and the at least one memory. The processor maybe configured to execute the computer-executable instructions to:receive data associated with a customer of a financial institution, thedata associated with at least one of (i) financial transaction data ofthe customer, or (ii) financial account data of the customer; identifyone of a plurality of segments for the customer, wherein the identifiedsegment is based at least in part on a first portion of the input data;calculate a current value of the customer, the current value based atleast in part upon one or more current holdings of existing products orservices by the customer with the financial institution; calculate afuture value of the customer, the future value based upon a probabilityof purchase for at least one product or service and a measure ofprofitability for the at least one product or service, the measure ofprofitability identified based at least in part from the identifiedsegment for the customer; and determine, based at least in part on acombination of the current value and the future value, that the customeris eligible for at least one recommended next action, wherein eachrecommended next action includes a new product or service for thecustomer or a modification to an existing product or service of thecustomer.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 illustrates a block diagram of an example system that supportsone or more customer value optimization processes in accordance with anexample embodiment of the invention.

FIG. 2 illustrates a flow diagram for an example process for performingone or more optimization processes, according to an example embodimentof the invention.

FIG. 3 illustrates an example implementation of a high-leveloptimization process, according to an example embodiment of theinvention.

FIG. 4A illustrates an example implementation of a process forproduct/service origination optimization, according to an exampleembodiment of the invention.

FIG. 4B illustrates an alternative implementation of a process forproduct/service origination optimization, according to an exampleembodiment of the invention.

FIG. 5 illustrates an example implementation of a process forcalculating a future value, according to an example embodiment of theinvention.

FIG. 6 illustrates an example implementation of a process forrelationship optimization, according to an example embodiment of theinvention.

FIG. 7 illustrates an example implementation of a process forcalculating a future value, according to an example embodiment of theinvention.

FIG. 8 illustrates an example implementation of a process for revenueand/or cost improvement optimization, according to an example embodimentof the invention.

DETAILED DESCRIPTION

The invention will now be described more fully hereinafter withreference to the accompanying drawings, in which example embodiments ofthe invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexample embodiments set forth herein; rather, these example embodimentsare provided so that this disclosure will be thorough and complete, andwill fully convey the scope of the invention to those of ordinary skillin the art. Like numbers refer to like elements throughout.

Embodiments of the invention may provide systems and methods forcustomer value optimization. The customer value optimization can includeanalyzing certain transaction and/or non-transaction data of customerswith one or more predictive models to determine predictive modelingscores, values, or indicators. These one or more predictive modelingscores, values, or indicators can be used with other transaction ornon-transaction data of customers, either alone or with other derivedvalues/calculations, to provide certain optimizations. An example aspectof the optimizations can be identifying customers to target with respectto a particular product or service offering that the financialinstitution wishes to promote. Alternatively, one or moreproducts/services can be identified for use in targeting certaincustomers. In addition, other optimizations can identify certainofferings or configurations of products or services that can be offeredto one or more customers in order to improve those customers'relationships with the financial institution.

It will be appreciated that the optimizations described herein, or atleast a portion thereof, can be performed by a service provider, afinancial institution, or a combination thereof on a periodic basis oron an as-requested basis, according to an example embodiment of theinvention. As an example, the service provider may operate as anapplication service provider (ASP) that allows a user of a financialinstitution computer to identify or provide data of customers to beanalyzed for optimizations, as well as any configuration options orpreferences for performing the optimizations. The results of theoptimizations can then be accessed by a financial institution computercommunicating with the service provider via a network. For example, thefinancial institution can have customer management software thatreceives the results of the optimizations performed by the serviceprovider. It will be appreciated that the service provider may provideoptimization functionality to a plurality of financial institutions,according to an example embodiment of the invention. It will also beappreciated that the service provider can also be a unit of a financialinstitution that provides the optimization functionality to one or moreunits of the same, a subsidiary of the same, or other financialinstitution(s) via networked financial institution computers.Alternatively, the optimizations described herein may also be performedby optimization software running locally at a financial institutioncomputer. In this regard, the financial institution computer can access,either locally or via a network, data for customers to be analyzed byoptimizations, as well as any configuration options or preferences forperforming the optimizations.

I. System Overview

FIG. 1 illustrates an example system 100 that supports one or morecustomer value optimization processes in accordance with an exampleembodiment of the invention. Although various computing devices and/orcomputers are illustrated in FIG. 1, it is appreciated thatcorresponding entities and/or individuals are associated with each ofthe computers illustrated. According to various embodiments, there maybe: one or more service provider systems 105, each associated with oneor more optimization computers 160, and one or more financialinstitution systems 106, each associated with one or more financialinstitution computers 140. According to various embodiments, there maybe any number of each entity type, and each entity may be associatedwith any number of suitable computers, computing devices, and/or otherdevices. For simplicity, the computers, devices, and/or entities may bereferenced in the singular, but it is appreciated that the samedescription applies to embodiments including multiple computers, devicesand/or entities. Similarly, for each of the computers described herein,it is appreciated that the computer may include any number of suitablecomponents and/or functionality. Moreover, although detaileddescriptions of system components are provided for the service providersystem 105, it is appreciated that any of the financial institutionsystems 106 may be configured in any suitable manner, which may besimilar to that described herein for the service provider system 105. Inthis regard, the financial institution computers can include one or morememory devices, processors, input/output (I/O) interfaces, and networkinterfaces. The one or more memory devices may store computer-executableinstructions, which may be accessed and executed by the one or moreprocessors to provide the functionality described herein with respect tothe financial institution computers.

As shown in FIG. 1, the service provider system 105 and the financialinstitution system 106 may be in communication with each other via oneor more suitable networks 145, which, as described below, can includeone or more separate or shared private and/or public networks, includingthe Internet or a publicly switched telephone network. In addition, theservice provider system 105, including at least an optimization computer160, can have access to one or more databases 110 a-n or other storageof data via one or more networks 144, which may be the same as ordifferent than networks 145. These components will now be discussed infurther detail.

The service provider system 105 may include any number of optimizationcomputers 160 that operate to receive certain transaction ornon-transaction data of customers of one or more financial institutions,and further perform one or more optimizations based at least in partupon the received data of the customers. In addition, the one or moreoptimization computers 160 may communicate with any number of financialinstitution computers 140 to receive options, preferences, and/orconstraints for performing one or more optimizations described herein,as well as to provide one or more results of the performed optimizationsto any number of financial institution computers 140. An optimizationcomputer 160 may be any suitable processor-driven device, such as, butnot limited to, a server computer, a mainframe computer, one or morenetworked computers, a desktop computer, a personal computer, a digitalassistant, a personal digital assistant, a digital tablet, an Internetappliance, an application specific circuit, a microcontroller, aminicomputer, or any other processor-based device. The execution ofsuitable computer-implemented instructions by the optimization computer160 may form a special purpose computer or other particular machine thatis operable to facilitate the processing of the optimizations, as wellas the receipt and output of data associated with the optimizations.Although a single optimization computer 160 is described herein, theoperations and/or control of the optimization computer 160 may bedistributed among any number of computers and/or processing components.

In addition to having one or more processors 164, the optimizationcomputer 160 may include one or more memory devices 162, one or moreinput/output (“I/O”) interfaces 166, and one or more network interfaces168. The memory devices 162 may be any suitable memory devices, forexample, caches, read-only memory devices, random access memory devices,magnetic storage devices, removable storage devices, etc. Additionally,any number of logical data storage constructs may be stored as desiredwithin the memory devices 162, such as any suitable database such as anoptimization database 170. In addition or in the alternative, whiledatabases 110 a-n may be accessed via a network 144 in some embodiments,any of databases 110 a-n may be stored within memory devices 162 withoutdeparting from example embodiment of the invention. The memory devices162 may further store a wide variety of data, such as by data files 172.Additionally, the memory devices 162 may store executable instructionsand/or various program modules utilized by the optimization computer160, for example, an operating system (OS) 174, a database managementsystem (“DBMS”) 176, an optimization processing module 180 and/or one ormore host modules 178.

The data files 172 and/or the optimization database 170 may include anysuitable data that facilitates the receipt or processing of certain datautilized with or by the optimization processing, which may includeoptions, preferences, and/or constraints to be utilized with theoptimization processing, as well as one or more results of any performedoptimization processing. For example, the data files 172 and/oroptimization database 170 may include data derived or received fromdatabases 110 a-n, any options, preferences or constraints received fromone or more financial institution systems 106, as well as processingresults from the performed optimizations that are made available to oneor more financial institution systems 106. The optimization processingmodule 180 may store predictive models, calculation algorithms,processing logic, and/or business rules utilized to perform one or moreoptimizations. In some example embodiments, the optimization processingmodule 180 may store processing logic, business rules, and/or othersoftware that is less prone to change over time, while other logic,rules, predictive models, and/or calculation algorithms that are morelikely to change or be modified may be stored in data files 172 and/oroptimization database 170. Many variations of the optimization database,data files 172, and/or optimization processing module 180 are availablein accordance with example embodiments of the invention. It isappreciated that the illustration of an optimization database 170 as aseparate database from the data files 172 and/or any other data storagemeans is provided for illustrative purposes, and that any data may bestored in any arrangement, separate or together with other data storedby the optimization computer 160.

The operating system (“OS”) 174 may be a suitable software module thatcontrols the general operation of the optimization computer 160. The OS174 may also facilitate the execution of other software modules by theone or more processors 164, for example, the optimization processingmodule 180 and/or the host module(s) 178. The OS 174 may be, but is notlimited to, Microsoft Windows®, Apple OSX™, Linux, Unix, or a mainframeoperating system. The host modules 178 may include any number ofsuitable host modules that manage interactions and communicationsbetween the service provider system 105 and external systems, such asfinancial institution system 106 (e.g., financial institution computer140). In this regard, the host module 178 can interface with othermodules such as optimization processing module 180 in order tofacilitate the receipt of data from databases 110 a-n, as well asoptions, preferences, and/or constraints from financial institutionsystem 106; and manage requests from one or more financial institutionsto perform one or more optimizations and/or requests to receive one ormore results from the performed optimizations. Additionally, in certainembodiments, the host modules 178 may be configured to generate and/orto present a wide variety of different interfaces and/or graphical userinterfaces, such as one or more interfaces that facilitate the receiptof data and/or requests from, or a presentation of results or otherinformation to the financial institution system 106 and/or serviceprovider system 105. An interface can be in the form of one or morebrowser-based or Internet-based webpages, although interfaces can alsobe presented through specialized software programs (e.g., stand-aloneapplication, applet, mobile device application, etc.), according to anexample embodiment of the invention. It will be appreciated that theinterface can be formatted for display on a mobile device (e.g.,personal communications device like a BlackBerry, iPhone, etc.) ornon-mobile device (e.g., desktop computer, server computer, etc.),according to an example embodiment of the invention. The interface maybe associated with security settings to enable access by certainregistered users of the service provider system 105 and/or financialinstitution system 106. As desired, a private interface may be brandedin accordance with specifications and/or preferences of a partnerentity. Additionally, as desired in certain embodiments, the hostmodules 178 may be configured to provide a web services interface layerto another entity or component of the system 100.

The optimization processing module 180 may be operable, configured,and/or programmed to receive data from optimization database 170 or datafiles 172 to calculate certain predictive modeling scores and/orcomputational values, to perform one or more optimizations based atleast in part on the predictive modeling scores and/or computationalvalues, and to provide one or more results of the performedoptimizations. Additional details of the operations of the optimizationprocessing module 180 and/or service provider system 105 operating logicand functionality are provided below with reference to FIGS. 2-8.

With continued reference to the optimization computer 160, the one ormore I/O interfaces 166 may facilitate communication between theoptimization computer 160 and one or more input/output devices; forexample, one or more user interface devices, such as a display, keypad,mouse, pointing device, control panel, touch screen display, remotecontrol, microphone, speaker, etc., that facilitate user interactionwith the optimization computer 160. The one or more network interfaces168 may facilitate connection of the optimization computer 160 to one ormore suitable networks, for example, the network(s) 144, 145 illustratedin FIG. 1, or local area network(s) within the service provider system105. In this regard, the optimization computer 160 may receive and/orcommunicate information to other components of the system 100, databases110 a-n (either directly or via one or more computers managing databases110 a-n) and financial institution systems 106. As desired, any numberof webpages, interface screens, and/or other presentations (e.g.,graphical user interfaces, etc.) may be provided or presented to afinancial institution system 106 via the network 145.

The databases 110 a-n can provide a variety of transaction andnon-transaction data associated with customers that are to be optimizedin accordance with the optimization processes described herein. In anexample embodiment of the invention, the databases 110 a-n may includeone or more of an electronic funds transfer (EFT) database 110 a, a billpayment database 110 b, an account processing database 110 c, and one ormore other source databases 110 n. In an example embodiment of theinvention, each of databases 110 a-n may include the followinginformation illustrated below.

EFT database 110 a

-   -   Debit card transaction data    -   i. Transaction type: May indicate a debit (e.g., a purchase) or        credit (e.g., a return) transaction. May also indicate whether        the transaction is a signature-based transaction (e.g., via        credit card network) or a PIN-based transaction (e.g., via an        EFT or ATM network).    -   ii. Transaction channel: Point of sale, online, etc.    -   iii. Merchant identification: May identify a merchant by name or        other identifier. May also identify a category for the merchant        (e.g., grocery, drug store, restaurant, gas station, etc.).    -   iv. Transaction date: Indicates a date for the transaction.    -   v. Purchase (e.g., debit) or refund (e.g., credit) amount    -   Automated Teller Machine (ATM) transaction data    -   i. Transaction type: May indicate a deposit transaction, a        withdrawal transaction, a balance inquiry transaction, or        another type of transaction.    -   ii. ATM identification/location: May identify the particular ATM        by location (e.g., address) or other identifier.    -   iii. Transaction channel: ATM    -   iv. Transaction date: Indicates a date for the transaction.    -   v. Withdrawal amount or deposit amount    -   vi. Current account balance    -   Non-transaction data    -   i. Customer identification (e.g., name, social security number,        address, telephone number, email address, and other contact        information etc.)    -   ii. Customer demographic information (e.g., age, sex,        occupation, etc.)    -   iii. Account establishment date: Establishment date for deposit        account underlying the debit card transaction data and/or ATM        transaction data    -   iv. Date of issuance of debit card or other transaction card    -   v. Contact/communication preferences (e.g., paper mailing,        email, etc.).

Bill payment database 110 b

-   -   Bill payment transaction data    -   i. Transaction type: Debit (e.g., payment to payee) or credit        (e.g., from payee)    -   ii. Transaction channel: Online, in-person, telephone, etc. In        addition, the particular user interface can be specified for        some transaction channels. For example, for an “online”        transaction channel, there may be a further specification of        whether the transaction originated from a website or application        of a particular financial institution or service provider.        Likewise, the online transaction channel can also indicate        whether a mobile device or associated mobile application was        utilized.    -   iii. Bill payment date    -   iv. Bill payment amount    -   v. Payee identifier: Identifies the payee of the bill payment        transaction.    -   vi. Source account: Indicates the source account (e.g., credit        card, debit card, deposit account, etc.)    -   vii. Number of failed bill payment transactions    -   viii. Amount(s) of failed bill payment transactions    -   ix. Date of failed bill payment transactions    -   Non-transaction data (e.g., account data)    -   i. Customer identification (e.g., name, social security number,        address, telephone number, email address, and other contact        information etc.)    -   ii. Customer demographic information (e.g., age, sex,        occupation, etc.)    -   iii. Bill payment enrollment date: Date that the customer        enrolled for bill payment service. If a customer enrolled for        bill payment services in conjunction with opening a bank        account, this bill payment enrollment date may be the same as        the bank account establishment date.    -   iv. Contact/communication preferences (e.g., paper mailing,        email, etc.).

Account processing database 110 c

-   -   Deposit account transactions    -   i. Transaction type: May indicate a deposit or withdrawal.    -   ii. Transaction channel: May indicate whether the deposit or        withdrawal involves teller, ATM, telephone, online (Internet),        or point of sale. In addition, the particular user interface can        be specified for some transaction channels. For example, for an        “online” transaction channel, there may be a further        specification of whether the transaction originated from a        website or application of a particular financial institution or        service provider. Likewise, the online transaction channel can        also indicate whether a mobile device or associated mobile        application was utilized. The transaction channel can also be        used to indicate internally generated transactions such as those        associated with late fees charged, charging of interest,        crediting of interest, etc., according to an example embodiment        of the invention.    -   iii. Merchant identification: Identifies any merchant involved        in the deposit or withdrawal transaction. The merchant can be        identified by a merchant name or other identifier. May also        identify a category for the merchant (e.g., grocery, drug store,        restaurant, gas station, etc.)    -   iv. Transaction date: Indicates a date for the transaction.    -   v. Deposit or withdrawal amount    -   vi. Current account balance    -   Loan product    -   i. Loan product type (e.g., fixed rate mortgage, adjustable rate        mortgage (ARM), home equity line of credit (HELOC), etc.)    -   ii. Loan product term    -   iii. Loan product open date    -   iv. Loan product opening balance    -   v. Loan product current balance    -   vi. Loan product monthly payment    -   vii. Loan product interest rate    -   viii. Delinquency period: 30 days, 60 days, 90 days, 120 days,        etc.    -   Time deposit    -   i. Time deposit product type (e.g., certificate of deposit (CD),        bond, Treasury note, etc.)    -   ii. Time deposit opening amount    -   iii. Time deposit current amount    -   iv. Time deposit interest rate    -   v. Time deposit term    -   vi. Time deposit open date    -   vii. Whether any early redemption/partial distribution has        occurred, and any associated redemption/distribution date and/or        amount.    -   Non-transaction data (e.g., account data)    -   i. Customer identification (e.g., name, social security number,        address, telephone number, email address, and other contact        information etc.)    -   ii. Customer demographic information (e.g., age, sex,        occupation, etc.)    -   iii. Account establishment date    -   iv. Contact/communication preferences (e.g., paper mailing,        email, ATM, teller, telephone, etc.).

Other source database 110 n

-   -   Transaction and non-transaction data from a variety of other        financial institutions and affiliated institutions, including        credit card institutions, credit reporting agencies, loan        providers/servicers, and the like.    -   Other transaction data and non-transaction data relating to        investment products, insurance products, and other        banking/financial institution products.

It will be appreciated that the transaction data and/or account data forone or more of respective database 110 a-n can be grouped according totime period, according to an example embodiment of the invention. Forexample, for account processing database 110 c, the January to Marchtransaction data can be combined to determine trends. Indeed, thegrouping of the data according to time periods can enable one or more ofthe predictive models or computations to be calculated per time periodsuch that differences or movements between time periods can beevaluated, according to an example embodiment of the invention.

Although not described or illustrated in detail, each financialinstitution computer 140 may be configured in the same or similar manneras described for the service provider system 105. For example, financialinstitution computer 140 may include one or more processor-basedcomputers operable to store and execute computer-executableinstructions, each having one or more processors, memories, I/Ointerfaces, network interfaces, operating systems, data files, databasesor other data storage means, DBMS, host modules and other operatinglogic to perform some or all of the same or similar functions as aredescribed herein with reference to the service provider system 105(e.g., optimization computer 160).

The networks 144, 145 may include any number of telecommunication and/ordata networks, whether public, private, or a combination thereof,including but not limited to, the Internet, a local area network, a widearea network, an intranet, intermediate handheld data transfer devices,public switched telephone networks, and/or any combination thereof andmay be wired and/or wireless. The networks 144, 145 may also allow forreal-time, off-line, and/or batch transactions to be transmittedthereover. Due to network connectivity, various methodologies describedherein may be practiced in the context of distributed computingenvironments. Although the system 100 is shown for simplicity asincluding networks 144, 145, it is to be understood that any othernetwork configuration is possible, which may optionally include aplurality of networks for each of networks 144, 145, each with devicessuch as gateways and routers, for providing connectivity between oramong networks.

Those of ordinary skill in the art will appreciate that the system 100shown in and described with respect to FIG. 1 is provided by way ofexample only. Numerous other operating environments, systemarchitectures, and device configurations are possible. Other systemembodiments can include fewer or greater numbers of components and mayincorporate some or all of the functionality described with respect tothe system components shown in FIG. 1. Accordingly, embodiments of theinvention should not be construed as being limited to any particularoperating environment, system architecture, or device configuration.

II. Operational Overview

FIG. 2 illustrates a flow diagram 200 for an example process forperforming one or more optimization processes, according to an exampleembodiment of the invention. Starting at block 205, the optimizationcomputer 160 can execute the optimization processing module 180, eitheralone or in conjunction with host module 178, to access, request, orreceive input data from one or more of databases 110 a-n via one or morenetworks 144. It will be appreciated that the data can be received on aperiodic basis, or on an as-requested basis, according to an exampleembodiment of the invention. As an example, at least a portion of thetransaction data and the non-transaction data of any EFT database 110 a,bill payment database 110 b, account processing database 110 c, or anyother source databases 110 n may be received by the optimizationcomputer 160. Accordingly, respective transaction data can be receivedfor customers from one or more of respective databases 110 a-n.Likewise, non-transaction data may be received for each customer fromdatabases 110 a-n, perhaps packaged in a respective customer informationfile (CIF) from respective databases 110 a-n. The transaction data andnon-transaction data may each include customer identificationinformation, or otherwise be received in a respective data package for arespective customer, such that the received data can be sorted,identified, or segregated on a customer-to-customer basis.

As described above, the transaction data may identify, for eachtransaction, a transaction type (e.g., deposit, credit, bill payment,etc.), a transaction date, a transaction channel, a transaction amount,and/or a merchant/payee identification associated with the transaction.The non-transaction data, which may be received in one or more CIFs, mayinclude data that is not transaction dependent, such as the customeridentification, customer date of birth, identification of the type ofaccount, and/or a date that the account was opened. It will beappreciated that the data received from databases 110 a-n for one ormore customers may correspond to data from a single financialinstitution or data from multiple financial institutions withoutdeparting from example embodiments of the invention.

Still referring to block 205, the data received from one or more ofdatabases 110 a-n may be normalized and/or converted, according to anexample embodiment of the invention. As an example, since databases 110a-n may store information in various disparate formats, it may benecessary to normalize the received data such the data is provided in astandardized format or an expected format according to an exampleembodiment of the invention. In this way, normalization can allow datato be received from virtually any number of databases associated withany financial or non-financial institutions that may manage thedatabases in different ways.

Normalization can ensure that data from databases 110 a-n (e.g.,customer identification, transaction type, transaction channel, etc) isprovided in a common, predefined format (e.g., number ofcharacters/numbers, spacing, etc.), and converted to be within anexpected range if appropriate. The normalization process, which includesproviding data in a common format and/or converting data, may alsoinclude performing simple counts, averages, or basic mathematicaloperations to derive certain basic values that may not be directlyspecified from the data received from one or more databases 110 a-n. Inthis regard, one or more of the following example values may be derivedas part of, or in conjunction with, the normalization or convertingprocess:

-   -   Transaction counts: A count of the number of transactions of a        certain type for one or more customer accounts within a time        period        -   Debit transaction count        -   Credit transaction count        -   Check transaction count        -   Point-of-sale transaction count        -   Online transaction count        -   Teller-assisted transaction count        -   Telephone-assisted transaction count        -   PIN-based card transaction count        -   Signature-based card transaction count    -   Product/service counts: Number of product/services of the        customer across one or more customer financial institutions        -   Debit card account count        -   Credit card account count        -   Count of number of loans        -   Count of number of deposit accounts (e.g., any of checking            accounts, savings accounts, money market accounts, etc.)        -   Time deposit product count (e.g., count of certificate of            deposit accounts)    -   Average amounts: An average of certain amounts across one or        more customer accounts within a time period        -   Average mortgage payment amount        -   Average loan payoff amount        -   Average balance across one or more debit cards, credit            cards, loan products, time deposit accounts, deposit            accounts, etc.        -   Average debit transaction amount        -   Average deposit transaction amount        -   Average check amount    -   Percentage amounts: Percentage calculations across one or more        products of the customer within a time period. Alternatively,        the percentages can also be expressed as ratios without        departing from example embodiments of the invention.        -   Percent of online transactions compared to transactions of a            set of channel types (e.g., online transactions, telephone            transactions, and in-person transactions, etc.)        -   Percent of PIN-based point of sale (POS) transactions            compared to transactions of a set of channel types (e.g.,            PIN-based POS transactions and signature-based POS            transaction).        -   Percent of signature-based POS transactions compared to            transactions of a set of channel types.        -   Percent of teller-assisted transactions compared to            transactions of a set of channel types.    -   Differences over a period of time: Can be expressed as a        mathematical difference or a ratio/percentage (e.g., (final        balance−initial balance)/initial balance).        -   Change in balance from one time period to another.        -   Change in transaction count from one time period to another.        -   Change in number of accounts from one time period to            another.

These and other counts, averages, or basic mathematical operations maybe performed at block 215 without departing from example embodiments ofthe invention. However, it will be appreciated that one or more of thesecounts, averages, or other basic mathematical operations may also beperformed as part of another subsequent block such as block 215 as well.Still referring to block 205, the data received from one or moredatabases 110 a-n, including any values computed as part of, or inconjunction with, the normalization or converting process, may be storedin an optimization database 170 and/or data files 172 for subsequentaccess, according to an example embodiment of the invention. It will beappreciated that the stored data in the optimization database 170 and/ordata files 172 can identify a respective plurality of customers, andcorresponding transaction data and non-transaction data for each of theplurality of customers. It will also be appreciated that different typesof data can be obtained by or otherwise received in database 170 and/ordata files 172 according to various timings, according to an exampleembodiment of the invention. For example, transaction data from any ofdatabases 110 a-n may be obtained by or otherwise received in database170 and/or data files 172 daily (perhaps on weekdays); on the otherhand, non-transaction data from any of databases 110 a-n may be obtainedby or otherwise received in database 170 and/or data files 172 on aweekly or monthly basis, or perhaps, only when a change in thenon-transaction data has occurred, according to an example embodiment ofthe invention.

Following block 205 are blocks 210 and 215, which may be performed inparallel, according to an example embodiment of the invention. However,in some example embodiments, the segmentation process of block 210 maybe performed prior to block 215 where the segment of a customer may beneeded for calculating certain predictive modeling scores orcomputational values at block 215.

Turning now to block 210, a segmentation process can be performed foreach customer using the stored data in optimization database 170 and/ordata files 172. In general, segmentation may be a process for separatinga population of customers into different groups—that is, “segments”—thatare expected to share one or more common characteristics or attributes.Accordingly, the transaction and/or non-transaction data may beprocessed or analyzed by the optimization computer 160 executing theoptimization processing module 180 to separate the plurality ofcustomers into respective segments. Each customer is typically assignedto a single segment, although in alternative embodiments, it is possiblefor a customer to be assigned to two or more segments. For example, amicro-segmentation approach may include many segments with respectivelimited attributes, such that a customer is expected to be assigned to aplurality of micro-segments, according to an example embodiment of theinvention. It will be appreciated that the number and identity ofrespective segments can be defined by a service provider, a financialinstitution, and/or a combination thereof. Segmentation for customersmay be utilized by the optimization processing module 180 in order toreduce the number of false positives that may occur based otherwise onindividual consideration of each customer, according to an exampleembodiment of the invention. Accordingly, the optimization processesdescribed herein may be tailored towards certain segments of customers.However, it will be appreciated that segmentation may not necessarily beutilized with or required with the optimization processes in alternativeembodiments of the invention.

Table I below identifies several example segments that may be possiblein accordance with example embodiments of the invention. It will beappreciated that the names and attributes of the example segments beloware provided for illustrative purposes only and that many variations areavailable without departing from example embodiments of the invention.Likewise, the number of available segments in a set of segments can bevaried in accordance with example embodiments of the invention.

TABLE I Segment Example Segment Number Description ExampleCharacteristics or Attributes Segment #1 Branch Centric Primary channelis in-person at a particular branch of a Churners financial institution.Rarely or never utilizes online services, and receives mostcorrespondences via paper mailing. Branch centric churners may have thehighest risk of attrition and the highest cost to service compared toother segments. Many branch centric churners are single-servicecustomers. These branch centric churners may be risky in terms of DDAcharge-offs. High incidence of non-Sufficient Funds (NSF - itemsreturned to payee) and overdraft (with item being paid). Segment #2 CashConstrained Cash constrained borrowers may be characterized as Borrowersmulti-channel users, but who prefer in-person banking. These cashconstrained borrowers have both deposit and lending relationship withfinancial institution; however, they are more likely than other customersegments to experience delinquencies and charge-off (on the lendingside), and to experience higher than average NSF/Overdrafts (depositside). Cash constrained borrowers may be multi-product households, butmay not utilize online banking services or utilize electronic payments(debit cards). Segment #3 Engaged Loyalists Engaged loyalists may becharacterized by very deep deposit and lending product relationshipswith the financial institution, with very high balances in each depositor lending product. These engaged loyalists may be highly engaged withthe financial institution using branches, online banking, electronicbill pay, debit cards and other ancillary services. The engagedloyalists may be the most profitable segment as well, as they have lowrisk of attrition, loan delinquencies, or charge-offs. Segment #4 YoungDigerati Young Digerati may be characterized by very high transactionlevels across all electronic channels, with very few transactions atbranch offices. The Young Digerati may also be heavy ATM/POS users.Balances for Young Digerati may be “average” compared to other segmentsand the depth of the relationship with the financial institution ismoderate. The may be a moderate risk of attrition for Young Digerati.Segment #5 Relationship The Relationship Agnostic may include financialAgnostic consumers with above average risk of attrition, and low-engagement with financial institution. The relationship agnostic mayhave very limited number of products and services used with thefinancial institution. The Relationship Agnostic may be price sensitiveon the lending side, and fee sensitive on the deposit side.

As another example, alternate segments can be utilized. These segmentsmay indicate a likelihood of a customer being persuaded to respond tomarketing efforts in a general sense. In this scenario, the segments caninclude those that are (1) persuadable, (2) would buy in any event,thus, not requiring much or any persuading, (3) would never buy, or (4)would be prone to annoyance so that a marketing effort may backfire. Inyet another example, the segments may divide those customers who aretechnologically proficient and those who are not. In still yet anotherexample, the segments can divide customers by income levels and/or assetlevels. Many variations of segments are available without departing fromexample embodiments of the invention.

Still referring to block 210, an example K-means algorithm may beutilized as the segmentation process to determine which segment acustomer should be assigned to. It will be appreciated that the exampleK-means algorithm may be a “directed” or “supervised” method ofclustering because the number of clusters—in this case, segments—may bespecified by a user. However, non-directed or self-organizing algorithms(e.g., Kohonen Neural Net) may also be utilized for the segmentationprocess without departing from example embodiments of the invention.

An example K-means algorithm is illustrates as follows:

${J = {\sum\limits_{j = 1}^{k}{\sum\limits_{X_{n} \in S_{j}}{{X_{n} - \mu_{j}}}^{2}}}},$

where J is the calculated K-means distance; j is an index value from 1to k; and S_(j) refers the set of segments {S₁, S₂, . . . , S_(k)},which includes a plurality of segments S₁ to S_(k). In addition, meanμ_(j) is the mean of points for a segment S_(j), and vector X_(n) may bedefined as a vector of variables of each customer, including, but notlimited to, one or more of: number of accounts, account balances, numberof transactions, channel used, frequency of channel usage, preferencefor certain channels, risk (e.g., risk of default/risk of non-sufficientfunds (NSF)/risk of overdrawing (OD) account/Attrition risk, etc.),purchase and spending behavior, etc. It will be appreciated that thevariables included within may be selected based upon their known orexpected ability to contribute to meaningful segmentation results,according to an example embodiment of the invention.

The K-means algorithm may follow an iterative process. First, when thecustomer variables are considered for all of the segments such that theK-means distance J is calculated for each segment, each customer will beassigned to the segment in which the K-means distance J is the smallestvalue compared to the K-means distances J for the other segments. Inother words, each segment may be represented by a respective centroidhaving a mean μ_(j). The respective variables for the customers willplace respective customers within a respective distance of the meanμ_(j) for a centroid of a segment. Following the assignment step, a newmean μ_(j) may be calculated for each centroid based upon the respectivevariables for customers assigned to the centroid, and the process abovemay be repeated until the customer assignments to each centroid (andthus, the segment) no longer change.

It will be appreciated that the K-means algorithm is only an examplealgorithm that can be utilized for block 210, and that other algorithmscan be utilized for the segmentation without departing from exampleembodiments of the invention.

Turning now to block 215, one or predictive modeling scores and/orcomputational values may be calculated for each customer that may be thesubject of one or more optimization processes. It will be appreciatedthat the particular modeling scores and/or computational values that arecalculated may be based upon the scores or values required by orotherwise specified by the algorithms for the supported optimizationprocesses according to an example embodiment of the invention. However,it will be understood that many other modeling scores and/orcomputational values are possible, as desired or required, in accordancewith example embodiments of the invention.

In an example embodiment of the invention, many computational values canbe calculated for each customer based upon the transaction dataavailable for each customer, which is either available from data in theoptimization database 170 or data files 172, or otherwise computed orderived from such stored data. For example, as illustrated below, one ormore of the following example predictive modeling scores or examplecomputational values may be calculated for each customer at block 215.

Predictive Modeling Scores

-   -   Probability/propensity to buy financial product or service: A        probability that a customer will purchase a particular product        or enroll in a particular service.    -   Next most likely financial product or service: The product or        service having the highest “probability/propensity to buy”        within a set of products/services.    -   Attrition risk: Probability that the customer will close primary        account or terminate a service (e.g., Bill Pay) with the        financial institution within X days.

Computational Values

-   -   Current value (of customer): A monetary measure of value of a        customer for all current products or services that the customer        has with a financial institution.    -   Future value (of product or service): A monetary measure of        anticipated profitability of a customer for a future product or        service with a financial institution.    -   Future Value (of Customer): A monetary measure of anticipated        profitability of a customer for all future products or services        with a financial institution.    -   Share of Wallet: Financial institution's share of bill payments        or transfer for a customer (compared to all bill payments of the        customer or total transfers).    -   Value at Risk: Future monetary value of customer that        potentially could be lost.

Table II below illustrates example algorithms that may be utilized togenerate the predictive model scores or computational values describedabove. It will be appreciated that these example algorithms for thepredictive models or computations may have been derived in a prioranalytics/modeling process, prior to their utilization at block 215. Inthis regard, the prior analytics/modeling process for at least some ofthe algorithms may have involved a two-part process. In a first of thetwo-part process, a statistical modeler may obtain historical data,including transaction data and non-transaction data, from a variety ofdifferent sources, and including any of databases 110 a-n. It will beappreciated that the data sources from which historical data may bereceived may include data sources beyond those that are available foruse with block 215. In this regard, the data sources for the historicaldata may be from a number of financial institutions or non-financialinstitutions. Based upon the historical data, the statistical modelercan then identify those variables from the historical data that mayappear to be predictive. Once the statistical modeler identifies thepotentially predictive variables, the statistical modeler can then useone or more statistical tools (e.g., SAS) to produce, refine, or trainthe algorithm predictive model or computation. For example, thestatistical tool may indicate one or more variables that should not beincluded in the algorithm, and likewise provide one or more weightingsfor those variables that will be included in the algorithm. It will beappreciated that the algorithms utilized for the predictive modelsand/or computations may be represented in a wide variety of formatsinvolving equations, matrices, and the like, according to an exampleembodiment of the invention. Likewise, the algorithms for any of thepredictive models and/or computations can be modified or updated on aperiodic basis, or on an as-needed basis, according to an exampleembodiment of the invention. It will be appreciated that many variationsof the algorithms for the predictive models or computations shown inTable II are available without departing from example embodiments of theinvention.

TABLE II Predictive Modeling Score or Compu- tational Value AlgorithmProbability/ Propensity to buy Product or Service (e.g., a FinancialProduct or Service)      ${{\Pr ( {{Product}_{i}\text{/}{Service}_{i}} )} = \frac{e^{z_{i}}}{e^{z_{i}} + 1}},$  where: z_(i) = β₀ + β_(k) X_(i) i = (customer 1, customer 2, . . .customer n); z_(i) = the probability that customer i purchases theproduct or service in next N days; and X_(i) is a vector of variablesfor each customer. As an example, for certificate of deposit (CD)product, the vector of variables may include the number of directdeposits within a certain number of months, average account balance,etc. As another example, for a loan product, the vector of variablesX_(i) may include debit transaction count, ATM debit amount, point ofsale count over a certain number of months, Mortgage Payment Amount,Loan Payments Count, Time Deposit Count, Loan Payoff Amount over 6months, Irregular loan payment amount, Point of Sale Debit over acertain number of months, failed bill payment Amount, Percent ofPIN-based Point of Sale transactions, and/or Percent of OnlineTransaction. It will be appreciated that these and other variables maybe selected based upon an expected contribution to the predictive natureof the Probability/Propensity to buy Financial Product or Service.According to an example embodiment, the weighting factors β₀, β_(k)utilized for z_(i) may be determined using statistical analysis (e.g.,one or more regressions) on a set of historical data involving thevector of variables X_(i). In this regard, an actual historicalprobability z_(i) (the probability of purchase) is known for a customerbased upon the historical data. In this way, the weighting factors β₀,β_(k) can be determined using statistical analysis in order to fit thevariables X_(i) to a known probability, according to an exampleembodiment of the invention. Next Most First calculate the“probability/propensity to buy” values Likely for a set ofproducts/services. Then, select product/ Financial service having thehighest probability from the set of Product or products/services basedupon respective probability/ Service propensity to buy values. CurrentValue (of Product or Service) Current Value = ((Current Product orService Balance) * (Profitability Value)) − Cost Value In an exampleembodiment of the invention, the Current product or service balance istypically available for a customer based upon the stored data indatabase 170 or data files 172. The profitability value may be basedupon analysis of a product or service profitability across a population.For example, the profitability value may be based upon at least arevenue assumption or value known as a net interest margin (NIM). Asanother example, the profitability value may be an industry benchmarkvalue (e.g., a NIM value) that is obtained from an external entity andstored for subsequent access in database 170 or data files 172. Theprofitability value, which is associated with a particular product orservice, may apply to all customers, or may apply only to customers in aparticular customer segment. The profitability value can also include oraccount for fees and services charges paid by the customer. The costvalue indicates a cost assumption associated with acquiring or servicingthe product or service. In an example embodiment of the invention, thecost value may be based upon analysis of the product or service costs,perhaps across a population of a particular financial institution. Thecost value of a product or service may be further dependent on thecustomer segment because the cost value of a product or service maydiffer from one customer segment to another; however, the cost value canbe independent of the customer segment as well. In an alternativeembodiment of the invention, the cost value may also be an industrybenchmark value that is obtained from an external entity and stored forsubsequent access in database 170 or data files 172. Current CurrentValue (of Customer) = Summation for Value respective current values forall products or services for (of Customer) the customer, as follows:${{CV}_{i} = {\sum\limits_{j = 1}^{M}V_{ij}^{c}}},$ where i = customer1, customer 2 . . . , customer n; j = product 1, product 2, . . . ,product M for customer i; and V_(ij) ^(c) is the current value ofproduct j for customer i, according to an example embodiment of theinvention. Future Value Future Value = (Probability/Propensity to BuyProduct (of Product or Service) * Projected Balance as derived from theor Service) segmentation * Net Interest Margin Projected Value × (1 −Attrition Risk)) − Projected Cost Value The Probability/Propensity toBuy Product or Service, as well as the Attrition Risk, may be calculatedas described herein. The Projected Balance may be based upon analysis ofhistorical balance of a population of customers, perhaps on a customersegment-by-customer segment basis, for a particular financialinstitution. In this regard, a respective Projected Balance may beavailable for each customer segment. The Projected Cost value may beassociated with a cost of acquiring or servicing the future product orservice. The Projected Cost value can also be based upon analysis ofhistorical cost information for a population of customers, perhaps on acustomer segment basis, for a particular financial institution. However,it will be appreciated that the Projected Balance and Projected CostValue can also be determined independent of a customer segment as well.The Net Interest Margin Projected Value may generally be a measure ofrevenue or profitability (e.g., expressed as a percentage to be appliedto a balance); it will be appreciated that other measures of revenue orprofitability can be utilized instead of simply a net interest margin.The Net Interest Margin Projected Value (or other measure ofprofitability) may be an industry benchmark value that is obtained froman external entity and stored for subsequent access in database 170 ordata files 172. Accordingly, the Net Interest Margin Projected Value maynot be based upon customer data of the particular financial institution.However, it will be appreciated that in an alternative embodiment, theNet Interest Margin Projected Value may be determined based uponanalysis of historical net interest margins or other measures ofprofitability for a population of customers, perhaps on a customersegment by customer segment basis. However, it will be appreciated thatthe Net Interest Margin Projected Value can also be determinedindependent of a customer segment as well. Future Value Future Value (ofCustomer) = Summation for respective (of future values for all productsor services for the Customer) customer, as follows:${{FV}_{i} = {\sum\limits_{j = 1}^{M}V_{ij}^{f}}},$ where i = customer1, customer 2 . . . , customer n; j = product 1, product 2, . . . ,product M for customer i; and V_(ij) ^(f) is the future value of productj for customer i. Attrition Risk${\Pr ( {Attrition}_{i} )} = \frac{e^{z_{i}}}{e^{z_{i}} + 1}$  z_(i) = β₀ + β_(k) X_(i), where: i = (customer 1, customer 2, . . .customer n); z_(i) = the probability that customer i attrites (e.g.,leaves financial institution, closes account, terminates service, etc.)in next N days; and X_(i) is a vector of variables for each customer. Asan example, for a deposit account, the vector of variables may includethe number of direct deposits, average account balance, etc. As anotherexample, for a demand deposit account, the vector of variables mayinclude Deposit Account Balance, Tenure of Customer, Monthly CreditAmount, Monthly Debit Amount, Balance Change over X months, BalanceChange over Y months, Transaction Count Change over X months,Transaction Count Change over Y months, Number of Loans Account, Numberof Time Deposit Account, Number of Money Market Account, ACH DebitTransaction Amount, ACH Deposit Transaction Amount, ATM Debit AmountChange over X months, and/or Customer Age. It will be appreciated thatthese and other variables may be selected based upon an expectedcontribution to the predictive nature of the Attrition Risk. Accordingto an example embodiment, the weighting factors β₀, β_(k) utilized forz_(i) may be determined using statistical analysis (e.g., one or moreregressions) on a set of historical data involving the vector ofvariables X_(i). In this regard, an actual historical probability z_(i)(the attrition risk) is known for a customer based upon the historicaldata. In this way, the weighting factors β₀, β_(k) can be determinedusing statistical analysis in order to fit the variables X_(i) to aknown probability, according to an example embodiment of the invention.Share of Share of Wallet = (Customer bill payment or transfer Walletamounts from customer's own financial institution)/ (Total customer billpayment or transfer amounts to all financial institutions). Value Valueat Risk = (Future Value of Customer) * Attrition at Risk Risk

At block 220, the optimization computer 160 executing the optimizationprocessing module 180 may identify optimization objectives, constraints,and/or options or preferences. One or more of the optimizationobjectives, constraints, and/or options or preferences may be providedby a user of the service provider system 105, the financial institutionsystem 106, or a combination thereof. For example, the financialinstitution system 106 may provide certain optimization objectives,constraints, and/or options or preferences via network 145 to theoptimization computer 160 such that the optimization computer 160 cangenerate the desired results of the optimizations. Alternatively, alocal user of the optimization computer 160 can likewise enter, perhapsvia I/O interface 166, certain optimization objectives, constraints,and/or options or preferences such that the optimization computer 160can generate the desired results of the optimizations.

Examples of optimization objectives, constraints, and preferences oroptions are provided below for illustrative purposes only:

Example Optimization Objectives

-   -   Determine which product or service to offer a particular        customer.    -   Determine which customers should be offered a particular        product.    -   Determine what action(s) (e.g., promotions, offerings of        products/services, fee structures, configurations of        products/service) should be taken to improve a relationship        between a customer and a financial institution. In an example        embodiment, the improvement in relationship may be based at        least in part on one or more of: (i) reducing attrition risk        with the financial institution, (ii) increasing balances or fees        from existing products or services, or (iii) generating        additional relationships through additional products or        services.    -   Determine what actions(s) (e.g., promotions, offerings of        products/services, fee structures, configurations of        products/services) should be taken to improve revenue and/or        cost for a particular customer of a financial institution.    -   Migrate customers from a high-cost servicing model to a        lower-cost alternative without increasing the risk of attrition        or risk of default.    -   Reduce risk of attrition among customers in a particular segment        or micro-segment.

Example Constraints

-   -   Limited availability of product or service offerings to A, B, .        . . N products or services.    -   Cost of acquisition limited to a maximum of $X.    -   Target revenue/cost improvement of a minimum of $X or Y %.    -   Target only customers in Segment J.    -   Limit targeted customers to K number of customers.    -   Channel for target recommendation limited to Channel W (e.g.,        email, text message, online computer webpage presentation (e.g.,        as part of financial institution banking website, bill payment        website, etc.), telephone, or paper mailing, ATM presentation,        in-person teller offering, etc.).    -   Limit default/delinquency exposure.

Example Preferences or Options

-   -   Run optimization [now, scheduled on a particular date, or        periodically].    -   Provide results of optimizations (e.g., identifying customers        and recommended actions) in a particular output format. It will        be appreciated that many variations of output formats are        available, including XML file formats, PDF file formats,        database formats, or comma-separated variable (CSV) formats. It        will be appreciated that the output format may be based at least        in part on whether the results of the optimization may be        utilized with, accessed by, or imported into the financial        institution software. For example, tellers, customer service        representatives, or other agents/employees of the financial        institution may have ready access to the results of the        optimizations through the financial institution software.    -   Access results of the optimization via specified portal (e.g.,        secure-Internet portal access, dedicated program interface,        financial institution software, etc.).    -   Provide constraints to the optimization via specified portal        (e.g., secure-Internet portal access, dedicated program        interface, financial institution software, etc.).

Following block 220, is a loop among blocks 222, 225, and 227. At block222, the optimization computer 160 executes the optimization processingmodule 180 to facilitate the selection of a customer for optimizationfrom one or more available customers. The customer may be selected fromthe customers for which predictive modeling scores and/or computationalvalues are available from block 215. In addition, the availablecustomers may be limited by any constraints provided by block 220 (e.g.,only target customers in a particular segment).

At block 225, one or more optimization processes may be performed by theoptimization computer 160 executing the optimization processing module180. The one or more performed optimizations may be based upon the oneor more optimization objectives identified from block 220. As a resultof performing one or more optimization processes, one or morerecommended actions may be available for the customer. As will bedescribed herein, the one or more recommended actions can include anoffering for a product or service to the customer or a configuration ofa product or service, according to an example embodiment of theinvention. The recommended action can also identify one or more channelsfor contacting the customer. However, it will be appreciated that insome instances, there may be no recommended actions for the customer.The one or more recommended actions, including a recommendation of noaction, may be stored in database 170 and/or data files 172 inassociation with an identification of the customer.

Following block 225 is block 227, where a determination is maderegarding whether any additional customers should be subject to the oneor more optimization processes of block 225. If so, then processing mayreturn to block 222, where another customer is selected foroptimization. On the other hand, if no customers remain, then processingmay proceed to block 230.

It will be appreciated that many variations of the loop among blocks222, 225, and 227 are available without departing from exampleembodiments of the invention. According to one variation, anoptimization process may determine which customers should be offered aparticular product or service. In this scenario, the optimizationprocess may sort through all available customers to determine oridentify which customers meet the criteria for being offered aparticular product or service.

Once no additional customers remain at block 227, processing may proceedto block 230. At block 230, the one or more respective recommendedactions for one or more customers may be provided or output, perhaps inaccordance with previously received output format preferences. In anexample embodiment of the invention, the one or more recommended actionsfor one or more customers may be delivered to financial institutionsystem 106, including one or more financial institution computers 140,according to an example embodiment of the invention. One or moreemployees or computers 140 of the financial institution system 106 canthen carry out the one or more recommended actions, as desired orappropriate. It will be appreciated that the one or more recommendedactions may be provided to the customers according to channelpreferences inferred from prior customer data. For example, priorcustomer transaction data may be analyzed to determine which channel thecustomer utilizes most frequently. Example channels, as describedherein, can be associated with paper mailing, email, online, telephone,ATM, or in-person communications, or yet other communications, accordingto an example embodiment of the invention. The most frequently usedchannel can then be used, for example, when offering the customer aproduct/service in accordance with a recommended action.

FIG. 3 illustrates an example implementation of the optimization processfor block 225. It will be appreciated that many variations of FIG. 3 areavailable in accordance with example embodiments of the invention.Turning now to block 305, the optimization objectives, as previouslyidentified by block 220, may be received or retrieved. Similarly, atblock 310, the constraints and preferences/options, as likewisepreviously identified by block 220, may be received or retrieved. Itwill be appreciated that blocks 305 and 310 may be performed inparallel, or may otherwise be performed by a single block, according toan example embodiment of the invention.

Following block 310 are decision blocks 315, 325, and 335. Decisionblocks 315, 325, and 335 may be satisfied, for example, depending on theoptimization objectives received in block 305. For example, the decisionblock 315 relating to product/service origination optimization may besatisfied where a received optimization objective of block 305 is todetermine which product or service to offer a particular customer, or todetermine which customers should be offered a particular product orservice. If decision block 315 is satisfied, then the product/serviceorigination optimization process at block 320 may be performed, perhapsin accordance with any constraints or options/preferences received atblock 310.

Likewise, the decision block 325 relating to relationship optimizationmay be satisfied where an optimization objective of block 305 is todetermine what action(s) (e.g., promotions, offerings ofproducts/services, fee structures, configurations of products/service)should be taken to improve a relationship between a customer and afinancial institution. If decision block 325 is satisfied, then therelationship optimization process at block 330 may be performed, perhapsin accordance with any constraints or options/preferences received atblock 310.

In addition, the decision block 335 relating to revenue/cost improvementoptimization may be satisfied where an optimization objective of block305 is to determine what actions(s) (e.g., promotions, offerings ofproducts/services, fee structures, configurations of products/services)should be taken to improve revenue/cost for a particular customer of afinancial institution. If decision block 335 is satisfied, then therevenue/cost improvement optimization process at block 340 may beperformed, perhaps in accordance with any constraints oroptions/preferences received at block 310.

It will be appreciated that more than one optimization process 320, 330,340 may be performed for one or more customers, according to an exampleembodiment of the invention. It will further be appreciated that otheroptimization processes are available beyond those illustrated for blocks320, 330, and 340, according to an example embodiment of the invention.

FIG. 4A illustrates an example implementation for block 320 that isdirected towards product/service origination optimization, according toan example embodiment of the invention. It will be appreciated that FIG.4A may be utilized where an optimization objective is to determine whichproduct or service to offer to one or more customers. Turning now toFIG. 4A, at block 404, the customer under consideration can beidentified. In conjunction with identifying the customer, block 404 mayfurther identify or retrieve one or more previously calculated modelingscores and/or computational values from block 215. In addition, anyother input data, for example transaction and/or non-transaction data ofthe customer, that may be needed for performing the product/serviceorigination optimization may also be identified or retrieved at block404.

Following block 404 is block 406. At block 406, the segment associatedwith the customer may likewise be identified. It will be appreciatedthat the segment may have been previously determined for the customer atblock 210. Following block 406 is block 408. At block 408, the“probability/propensity to buy” values of the customer for a set ofproducts/services under consideration may be analyzed. As an example,the “probability/propensity to buy” values may be available for a set ofproducts/services, which may include a checking account, a savingsaccount, a money market account, an auto loan, a time deposit account(e.g., certificate of deposit (CD) account), auto loan, mortgage, creditcard, debit card, online banking service, and/or electronic bill paymentservice. It will be appreciated that many alternatives to the set ofproducts/services under consideration may be available in accordancewith example embodiments of the invention. An example aspect of block408 may be to determine whether a customer has a sufficiently highprobability/propensity to buy at least one product or service (or anyproduct within a class of products (e.g., loan products)) such that thecustomer should be considered for an offer for a product/service. Thus,it will be appreciated that the one or more threshold values of block408 may be set based upon one or more constraints identified by block310 (or similarly, block 220).

If all of the “probability/propensity to buy” values are below thethreshold value(s), then processing may proceed to block 410. At block410, the recommended action for the customer may be determined to be “NoAction”. On the other hand, if one or more of the“probability/propensity to buy” values do indeed meet or exceed certainthreshold value(s), then processing may proceed to block 412.

At block 412, the future value of the customer may be calculated inaccordance with the set of products or services under consideration. Itwill be appreciated that in some example embodiments, the future valueof the customer may have been previously determined at block 215.However, in other example embodiments where the future value of thecustomer is not already available, or where the future value of thecustomer was not calculated with respect to the same set of products orservices presently under consideration, then a process such as thatshown in FIG. 5 can be utilized to calculate the future value of thecustomer.

In particular, FIG. 5 illustrates an example implementation for aprocess 500 for calculating the future value of the customer. Turningnow to FIG. 5, at block 502, a product/service is identified from theset of products or services under consideration. In an exampleembodiment of the invention, the set of products or services underconsideration may be those products or services not currently held orutilized by the customer. However, in an alternative embodiment of theinvention, the set of products or services under consideration couldinclude products/services already held or utilized by the customer. Inthis case, the associated “probability/propensity to buy value” for aproduct/service already held or utilized by the customer may be zero orlow value since in some embodiments, a customer is unlikely to be ableto utilize multiple instances of a same product/service.

Following block 502 is block 505, where the “probability/propensity tobuy value” for the identified product/service is obtained or calculated,as discussed herein. The probability/propensity to buy value mayindicate the likelihood (e.g., a percentage) that the customer willbuy/utilize a product or enroll/utilize in a service in a certain periodof time.

At block 510, the projected product/service balance (B) for theidentified product/service is also obtained. The projectedproduct/service balance may indicate an expected balance if a customerwere to buy/utilize a product or enroll/utilize in a service in acertain period of time. In an example embodiment of the invention, theprojected product/service balance may have been obtained based upon aprior analysis of historical balances of customers in a customersegment. In this regard, the financial institution can determine theprojected product/service balance by analyzing customer balances forcurrent customers within a particular segment (e.g., determining an“average” balance by customer segment). In an alternative embodiment,the financial institution can also determine the projectedproduct/service balance by analyzing customer balances for currentcustomers irrespective of segment (e.g., by random sampling, for allcustomers, etc). Yet further, in another alternative embodiment, theprojected product/service balance may not be based upon a financialinstitution's data for its customers, but rather may be an industrybenchmark product/service balance that is obtained from an externalentity and stored for subsequent access in database 170 or data files172. An industry benchmark product/service balance may be determined byan external entity analyzing data from a variety of financialinstitutions, according to an example embodiment of the invention. Itwill appreciated that a combination (e.g., multiplication) of the“probability/propensity to buy value” and the “projected product/servicebalance” may provide an adjusted product/service balance that accountsfor the likelihood for purchase or enrollment, according to an exampleembodiment of the invention.

Following block 510 is block 515, where the projected revenue assumption(R) may be determined for the identified product or service. In anexample embodiment, the projected revenue assumption, which may beexpressed as a net interest margin value, may be the same for anyproducts/services for a customer in a particular segment. However, inother example embodiments, the projected revenue assumption may be basedupon both a segment and a particular product/service, or on a particularproduct but not a segment. The projected revenue assumption, whenapplied to or combined with (e.g. multiplied by) the projectedproduct/service balance (or adjusted product/service balance), maygenerate a measure of how much revenue is expected from the particularproduct or service. The projected revenue assumption may be an industrybenchmark value that is obtained from an external entity and stored forsubsequent access in database 170 or data files 172. Alternatively, theprojected revenue assumption can also be determined by a financialinstitution based upon analyzing historical revenue and/or profitabilitymeasures of its existing customers.

Following block 515 is block 520, where the acquisition cost (C) isdetermined. The acquisition cost generally refers to an amount that afinancial institution will need to spend in order to successfullyenroll, register, or sign up a customer for a product/service. In anexample embodiment, the acquisition cost may be the same for anyproducts/services for a customer in a particular segment. However, inother example embodiments, the projected acquisition cost may be basedupon both a segment and a particular product/service. In addition or inthe alternative, the acquisition cost may also be further based upon theexpected channel by which the product/service (e.g., email, paper mail,telephone, teller) will be offered to the customer. In this regard, thefinancial institution can determine the projected acquisition cost byanalyzing prior data for acquisition costs for current customers withina particular segment (e.g., determining an “average” acquisition cost bycustomer segment). In an alternative embodiment, the financialinstitution can also determine the acquisition cost by analyzingacquisition costs for current customers irrespective of segment (e.g.,by random sampling, for all customers, etc). Yet further, in anotheralternative embodiment, the acquisition cost may not be based upon afinancial institution's data for its customers, but rather may be anindustry benchmark value that is obtained from an external entity andstored for subsequent access in database 170 or data files 172. Anindustry benchmark product/service balance may be determined by anexternal entity analyzing data from a variety of financial institutions,according to an example embodiment of the invention.

At block 525, the future value may be calculated for a particularproduct or service for the customer at hand. In an example embodiment ofthe invention, the future value may be calculated as follows: FutureValue=(Probability/Propensity to Buy Value)*(Projected Product/ServiceBalance)*(Projected Revenue assumption)*(1-Attrition Risk)−AcquisitionCost, where the Attrition Risk was previously determined at block 215.It will be appreciated that the foregoing future value calculation isprovided by way of example, and that many other variations are availablewithout departing from example embodiments of the invention.

Following block 525 is block 530, which determines whether anyadditional products/services exist that still need a future valuecalculation. If so, then the process returns to block 502. Table IIIbelow illustrates example future values that are calculated for a set ofproducts/services based upon a respective Probability/Propensity to BuyValue, Projected Product/Service Balance, Projected Revenue assumption,Attrition Risk, and Acquisition Cost. In Table III, the ProjectedProduct/Service Balance and Acquisition Cost differs based uponproduct/service, but the Projected Revenue assumption may remain thesame for a particular customer segment. However, it will be appreciatedthat in other example embodiments, the Projected Revenue assumptioncould vary depending upon the product or service as well. Likewise, theAttrition Risk may have been determined independently of the customersegment, and may be the same for all products and services; however, theAttrition Risk can also vary based upon the product or service withoutdeparting from example embodiments of the invention.

TABLE III Projected Revenue assumption Future Value Product/ServiceProbability/ (e.g., Net (of Product/ under Propensity to ProjectedProduct/ Interest Margin) Attrition Acquisition Service) ConsiderationBuy Value (%) Service Balance (percentage) Risk (%) Cost Year 1 AutoLoan 60% $12,000 0.025 0.02 $125 $51.40 Home Equity 50% $26,000 0.0250.02 $100 $218.50 Line of Credit (HELOC) Credit Card 25% $7,500 0.025.02 $75 −$29.06

Having calculated the respective future values for each product/servicein the set under consideration, processing may proceed to block 535. Atblock 535, it may be determined whether the future value of the customerbased upon the set of products/services under consideration is needed.If not, then the processing of FIG. 5 may terminate. Otherwise, theprocessing may proceed to block 540. At block 540, the future value ofthe customer may be calculated. According to an example embodiment ofthe invention, the future value of the customer may be calculated as thesummation of respective calculated future values of theproducts/services under consideration. For example, if the set ofproducts consists of the 3 products in Table III (Auto Loan, HELOC, andCredit Card), then the Future Value of the Customer may be $240.84,which is calculated as $51.40 (for Auto Loan)+$218.50 (for HELOC)−$29.06(for Credit Card), according to an example embodiment of the invention.

Returning now to block 412 of FIG. 4A, the future value of the customerhas been determined via the process of FIG. 5, and processing mayproceed to block 415. Block 415 may determine whether the future valueof the customer exceeds a threshold value. An example aspect of block415 may be to determine whether a customer has a sufficiently highfuture value to be considered for one or more product/service offerings.The threshold value for block 415 may be set based upon one or moreconstraints identified by block 310 (or similarly, block 220).

If the customer future value does not meet the threshold value, thenprocessing may proceed to block 410, where the recommended action forthe customer may be determined to be “No Action”. On the other hand, ifthe customer future value meets or exceeds the threshold value, thenprocessing may proceed to block 420. At block 420, a list ofproduct(s)/service(s) with the highest future values may be determinedfor the customers. In block 420, the future values for theproduct(s)/service(s), which may have been determined as part of block412, may be used to identify a specified number of product(s)/service(s)with the highest future values. For example, if there are 7products/services in the set under consideration, perhaps only a portionof the set (e.g., 3 product/services) having the highest future valuesmay be selected. The desired number of products/services in the list maybe set based upon one or more constraints identified by block 310 (orsimilarly, block 220).

Block 422, which can occur in conjunction with or subsequent to block420, may remove from the list, those products or services already beingutilized by the customer. In this way, the customer is not offered aproduct or service that he or she may already have. In some exampleembodiments of the invention, block 420 may be optional. In particular,while the customer may already have a product or service, there may beconfiguration or customizations that can be provided to the existingproduct or service of the customer (See, e.g., block 430 describedbelow).

Following block 422 is block 425. At block 425, the customerrecommendation may be a product/service offering or recommendation basedupon the one or more products/services with the highest future value inthe list. For example, the product/service with the highest future valuein the list may be the one that will be offered to the customer. It willbe appreciated that the number of products/services included with thecustomer recommendation can be set based upon the options/preferencesidentified by block 310 (or similarly, block 220). It will also beappreciated that in some instances, block 422 may remove allproducts/services from the list. In this case, in block 425, therecommended action for the customer may be determined to be “No Action”,according to an example embodiment of the invention. The recommendedaction for a customer may be stored in association with a customeridentifier for subsequent access in database 170 and/or data files 172.

Following block 425 is block 430, where a determination is made withrespect to whether to customize the customer's offering of therecommended product(s)/service(s).

For example, if a loan product were to be recommended, the offeredinterest rate may need to be set. As another example, for many financialproducts to be recommended, there may be one or more fees that may needto be set (e.g., credit card annual fees, closing costs for loanproducts, deposit account fees, etc.). In addition, the customization ofthe offering can also include indicating a preferred channel by which tooffer the recommended product or service. In some example embodiments,the preferred channel may be set as an option by a financial institutionor service provider, perhaps in accordance with block 310 (or block220). In other example embodiments, the preferred channel may be basedat least in part upon an analysis of prior transaction data of thecustomer. For example, the preferred channel may be based upon the mostrecent channel indicated by the prior transaction data. Alternatively,the preferred channel may be based upon which channel is indicated by amajority of the prior transaction data of the customer within a timeframe. As another alternative, the preferred channel may be based uponwhether a channel is specified by the institution or service provider,and further indicated by at least one or more prior transactions of thecustomer. It will be appreciated that at block 435, any customization ofthe customer's offering of the recommended product(s)/service(s) may bestored in association with a customer identifier for subsequent accessin database 170 and/or data files 172.

FIG. 4B illustrates an alternative implementation for block 320 that isdirected towards product/service origination optimization. It will beappreciated that FIG. 4B may be utilized where an optimization objectiveis to determine which customers should be offered a particular productor service. Turning now to FIG. 4B, at block 450, the customer underconsideration can be identified. In conjunction with identifying thecustomer, block 450 may further identify or retrieve one or morepreviously calculated modeling scores and/or computational valuesdetermined in block 215. In addition, any other input data, for exampletransaction and/or non-transactional data of the customer, that may beneeded for performing the product/service origination optimization mayalso be identified or retrieved at block 450.

Following block 450 is block 452. At block 452, the segment associatedwith the customer may likewise be identified. It will be appreciatedthat the segment may have been previously determined for the customer atblock 210. Following block 452 is block 455. At block 455, a particularproduct/service under consideration for an offering may be identified.It will be appreciated that one or more particular products/services mayhave been identified as a constraint at block 310 (or similarly, block220).

Following block 455 is block 460. Block 460 may determine whether thecustomer already owns or utilizes the particular product or serviceidentified from block 455. An example aspect of block 460 may be toreduce an irrelevant or duplicative offering by eliminating fromconsideration, those customers that already have the particular productor service under consideration for an offering. Accordingly, if block460 determines that the customer already owns or utilizes the particularproduct or service, then processing may proceed to block 465, where therecommended action for the customer may be determined to be “No Action”.

On the other hand, block 460 may determine that the customer does notalready own or utilize the particular product or service, and processingmay proceed to block 470. Block 470 may obtain the“probability/propensity to buy” value associated with the particularproduct/service for the customer. The “probability to buy” value mayhave been previously calculated as part of block 215. Block 470 may alsoobtain the one or more threshold values by which the “probability tobuy” value may be compared to The one or more threshold values may bestatic in one embodiment. However, in another embodiment, the one ormore threshold values may be dynamic, for example, when obtained or setby one or more constraints or preferences at block 310 (or similarly,block 220). An example aspect of block 460 may be to determine whether acustomer has a sufficiently high probability/propensity to buy for theparticular product or service (compared to the one or more thresholdvalues) such that the customer should be considered for an offer for theparticular product/service.

If at block 470, the “probability/propensity to buy” value for theparticular product/service does not meet a certain threshold value suchthat a determination is made that there is not a highprobability/propensity to buy, then processing may proceed to block 465where the recommended action for the customer may be determined to be“No Action”.

On the other hand, at block 470, the “probability/propensity to buy”value for the particular product/service may meet or exceed a certainthreshold value such that a determination is made that there is a highenough probability/propensity to buy value, and processing may proceedto block 472. At block 472, the future value for the particularproduct/service may be determined. It will be appreciated that thefuture value for the particular product/service may have been previouslydetermined at block 215, and if so, the future value may be obtainedfrom database 170 and/or data file 172. On the other hand, if the futurevalue is not available, then it may be calculated in accordance withFIG. 5, discussed herein, according to an example embodiment of theinvention.

Following block 472 is block 475, where a determination is made as towhether the future value for the particular product or service exceedsone or more thresholds. The one or more threshold values may be staticin one embodiment. However, in another embodiment, the one or morethreshold values may be dynamic, for example, when obtained or set byone or more constraints or preferences at block 310 (or similarly, block220). An aspect of block 475 may be to determine whether the futurevalue of the particular product or service is sufficiently high in orderto proceed with an offering of the particular product or service of thecustomer. Accordingly, if at block 475, the future value for theparticular product or service does not exceed the threshold, thenprocessing may proceed to block 465, where the recommended action forthe customer may be determined to be “No Action”.

On the other hand, block 475 may determine that the future value for theparticular product or service does meet or exceed the threshold, inwhich case processing may proceed to block 480. At block 480, theparticular product or service under consideration may be approved forrecommendation to the customer. The recommended product or service forthe customer may be stored in association with a customer identifier forsubsequent access in database 170 and/or data files 172.

Following block 480 is block 485. At block 485, a determination is madewith respect to whether to customize the customer's offering of therecommended product or service, as similarly discussed above withrespect to block 430. If there is any customization of the customer'soffering of the recommended product or service, then thosecustomizations may be stored in association with a customer identifierfor subsequent access in database 170 and/or data files 172.

FIG. 6 illustrates an example implementation of block 330 that isdirected towards relationship optimization, according to an exampleembodiment of the invention. It will be appreciated that FIG. 6 may beutilized where an optimization objective is to determine what action(s)(e.g., promotions, offerings of products/services, fee structures,configurations of products/service) should be taken to improve arelationship between a customer and a financial institution, or to makea particular benefit (e.g., free month of service for example) availableto certain customers only. Benefits can be either of a “hard” nature(e.g., an offered product or service), or of a “soft” nature (preferredstatus in branches, guaranteed routing to a live-CSR in a call centerrather than a voice-response-unit (VRU), free investment newsletter,etc). Turning now to FIG. 6, at block 602, the customer underconsideration can be identified. In conjunction with identifying thecustomer, block 602 may further identify or retrieve one or morepreviously calculated modeling scores and/or computational valuesdetermined at block 215. In addition, any other input data, for exampletransaction and/or non-transactional data of the customer, that may beneeded for performing the product/service origination optimization mayalso be identified or retrieved at block 602.

At block 604, the segment associated with the customer may likewise beidentified. It will be appreciated that the segment may have beenpreviously determined for the customer at block 210. Following block 604is block 606. At block 606, one or more thresholds relating to a currentvalue and/or future value of the customer may be identified. These oneor more thresholds may have been set based upon one or more constraintsidentified by block 310 (or similarly, block 220). The one or morethresholds may be subsequently utilized in block 610 to determine anextent to which relationship improvement actions may be provided basedupon the current value and/or future value of a customer.

Following block 606 is block 608, where the current value and the futurevalue of the customer may be calculated. It will be appreciated that insome example embodiments, the current value and the future value of thecustomer may have been previously determined at block 215. However, insome example embodiments, the current value and/or future value of thecustomer may not already be available, or the current value and/orfuture value may not have been calculated with respect to a same set ofproducts or services. As such, if the current value is not currentlyavailable, then a process such as that shown in FIG. 7 may be utilizedto calculate the current value.

Turning now to the process 700 of FIG. 7, at block 705, a current orexisting product or service is identified. The current or existingproduct or service may be limited to those of the customer with aparticular financial institution, perhaps one that is seeking to improveits relationship with the customer, according to an example embodimentof the invention. However, alternative embodiments, the current orexisting product or service may be scoped to two or more financialinstitutions without departing from example embodiments of theinvention. Following block 705 is block 710, where the current balanceof the identified product or service is identified, perhaps byretrieving such balance information from database 170 and/or data files172. Following block 710 is block 715, where a revenue assumption (R)may be identified for the identified product or service. The revenueassumption, when applied to the projected product/service balance, maygenerate a measure of how much revenue may be generated from theparticular/product service. In an example embodiment of the invention,the determined revenue assumption may be an industry benchmark valuethat is obtained from an external entity and stored for subsequentaccess in database 170 or data files 172. The revenue assumption may bethe same for the particular product/service irrespective of the customersegment, or it may differ from one customer segment to another. Forexample, the revenue assumption may be a first value for a particularproduct/service held by a first customer in a first segment, but may bea second value for the particular product/service held by a secondcustomer in a second segment different from the first segment.

Following block 715 is block 720. At block 720, the cost assumption (C)associated with the particular product/service with the customer isidentified. The cost assumption may generally refer to an amount thatthe financial institution spends to service or maintain theproduct/service with the customer. The amount can be a fixed amount, orit may be represented as a percentage or proportion of another valuesuch as a balance or transaction volume. In an example embodiment, thecost assumption can be the same for the particular product/serviceirrespective of the customer segment, or it may differ from one customersegment to another. The cost assumption may be an industry benchmarkvalue that is obtained from an external entity and stored for subsequentaccess in database 170 or data files 172.

At block 725, the current value may be calculated for a particularproduct or service. In an example embodiment of the invention, thefuture value may be calculated as follows: CurrentValue=(Balance*Revenue assumption)−Cost Assumption. The current valuemay be stored in association with an identification of the customer anda product/service in database 170 and/or data files 172, according to anexample embodiment of the invention.

Following block 725 is block 730, which determines whether anyadditional products/services of the customer still need a current valuecalculation. If so, then the process returns to block 705. Table IVbelow illustrates example current values that are calculated forexisting product/services of a customer.

TABLE IV Current Value Existing Revenue Cost Annualized (ofProduct/Service Balance Assumption Assmption Product/Service) Checking$5,375 0.020 $25 $82.50 Account Credit Card $9,854 0.043 $50 $373.72Money Market $1,200 0.023 $20 $35.20

Having calculated the respective current values for each product orservice of the customer, processing may proceed to block 735. At block735, it may be determined whether the current value of the customer isneeded. If not, then the processing of FIG. 7 may terminate. Otherwise,the processing may proceed to block 740. At block 740, the current valueof the customer may be calculated. According to an example embodiment ofthe invention, the current value of the customer may be calculated asthe summation of the respective calculated current values of the currentproducts or services of the customer. For example, if the customer hasthe 3 products in Table IV (Checking Account, Credit Card, and MoneyMarket), the Current Value of the Customer may be $491.42, which iscalculated as $82.50 (for Checking Account)+$373.72 (for CreditCard)+$35.20 (for Money Market), according to an example embodiment ofthe invention. It will be appreciated that the current value of thecustomer may be stored in association with an identification of thecustomer in database 170 and/or data files 172, according to an exampleembodiment of the invention. It will be appreciated that many variationsof FIG. 7 are available in accordance with example embodiments of theinvention.

Returning now to block 608 of FIG. 6, the current value of the customermay haven been determined or otherwise calculated as discussed above.Likewise, the future value of the customer may be determined, perhaps inaccordance with FIG. 5, discussed herein. It will be appreciated that insome example embodiments, the set of products/services utilized incomputing the future value of the customer may exclude thoseproducts/services already held or utilized by the customer with thefinancial institution. Indeed, the set of products/services utilized incomputing the future value of the customer may include additionalproducts/services not currently held or utilized by the customer withthe financial institution. However, even if the existingproducts/services are included within the set of products/services,other factors such as the “propensity to buy product/service” may besignificantly lower (or zero) for those existing products/services suchthat the inclusion of existing products/services will not have amaterial effect on the calculated future value of the customer

Following block 608, processing may proceed to block 610. At block 610,the one or more identified thresholds or criteria of block 606 may beapplied to the calculated current value/future value of the customer todetermine an overall value of the customer. The overall value of thecustomer can determine which actions are available for the customer. Itwill be appreciated that there are may be various methods of applyingone or more thresholds to the current value/future value of the customerto determine the overall value of the customer. In one exampleembodiment of the invention, the current value and the future value canbe mathematically combined to generate an estimated total value. Forexample, the current value and the future value, which may berespectively weighted with weighting factors if necessary, may be summedtogether to provide an overall or total customer value. The totalcustomer value may then be subject to thresholds to determine whetherthe customer's total value indicates one of the following 3 examplegradations: low value (e.g., total customer value<Threhold1), mediumvalue (e.g., total customer value (e.g., Threshold1≧total customervalue>Threshold2), or high value (e.g., total customervalue≧Threshold2). It will be appreciated that fewer or additionalthresholds may be utilized to provide fewer or additional gradationswithout departing from example embodiments of the invention (e.g., 1threshold=2 gradations; 3 thresholds=4 gradations, etc.).

According to an alternative example embodiment of the invention, thetotal customer value can likewise be classified or separated intoexample gradations using an alternate example process. For example, oneor more thresholds can be applied to the calculated current customervalue to determine which of the following 3 example discrete levels thecurrent customer value may be classified under: (i) a high currentcustomer value, (ii) a medium current customer value, or (iii) a lowcurrent customer value. Similarly, one or more thresholds can be appliedto the calculated future customer value to determine which of thefollowing 3 example discrete levels the current customer value may beclassified under: (i) a high future customer value, (ii) a medium futurecustomer value, or (iii) a low future customer value. In this example,there are 3 possible discrete levels for classifying the currentcustomer value and the future value, thereby providing for 9 possiblecombinations of discrete levels for the current value and the futurevalue. Each of these 9 possible combinations can be a respectivegradation, according to an example embodiment of the invention.Alternatively, the 9 possible combinations can be separated into thedesired number of gradations. For example, a low-value customergradation can encompass a combination of a low current value and a lowfuture value. A high-value customer gradation can encompass acombination of a high current value and a high future value. Amedium-value customer gradation can encompass the remaining 7 possiblecombinations, according to an example embodiment of the invention.

Following block 610 may be some variation of blocks 612 and 614. Inparticular, blocks 612 and 614 may define an extent to which aparticular customer is provided with a recommended action based upon theparticular gradations determined at block 610 for the customer. Forexample, block 612 may determine that the customer is a low-valuecustomer, in which case processing may proceed to block 622, where therecommended action may be set to “None” to indicate that no action isrecommended. On the other hand, block 612 may determine that thecustomer is not a low-value customer, in which case processing mayproceed to block 614.

Block 614 may determine that the customer is a high-value customer inwhich case processing may proceed to block 616. Block 616 may determinewhether the high-value customer has certain attributes desired foreligibility for one or more recommended actions. For example, block 616may determine whether the customer has one or more of high ongoingdeposits, high liquid assets, or high ongoing outflows. The ongoingdeposit, liquid asset, or outflow level may be determined by analyzingthat data from one or more databases 110 a-n, perhaps data from at leastaccount processing database 110 c, according to an example embodiment ofthe invention. If the customer does have the required attributes ofblock 616, then processing may proceed to block 618. Block 618 maydetermine whether the customer already has the investment/trust servicesthat are to be recommended, and if so, processing may proceed to block622, where the recommended action may be set to “None”. On the otherhand, if block 618 determines that the customer does not already havethe investment/trust services, then processing may proceed to block 620,where the recommended action is set to “Refer to investment/trustservices.” Example investment/trust services may involve referring thecustomer to an investment advisor to assist in setting up one or morebrokerage accounts, mutual funds, annuities, trusts, insurance products(e.g., variable or whole life insurance products with investmentcomponents), estate planning, and the like.

It will be appreciated that the investment/trust services of blocks 618and 620 are provided by way of example only, and that otherproducts/services may be provided as recommended actions for high-valuecustomers, according to an example embodiment of the invention.

On the other hand, block 614 may determine that the customer is not ahigh-value customer, which in combination with block 612, implies thatthe customer is a medium-value customer. In this case processing mayproceed to block 624. Block 624 may determine whether there is a highvalue at risk at stake. In particular, block 624 may obtain the value atrisk score calculated at block 215 for comparison to a threshold value.The threshold value may be static in one embodiment. However, in anotherembodiment, the threshold value may be dynamic, for example, whenobtained or set by one or more constraints or preferences at block 310(or similarly, block 220). If the value at risk score is not higher thanthe threshold—that is, there is not a high value at risk—then processingmay proceed to block 622, where the recommended action may be set to“None”. This approach may be desirable in a situation where resourcesmay not be desirable to spend on a customer in which there is not a highvalue at risk.

On the other hand, block 624 may determine that there is indeed a highvalue at risk (e.g., the value at risk score exceeding the threshold),and processing may proceed to block 626. At block 626, a set ofeligibility rules for one or more actions may be obtained based upon atleast one of (i) the customer segment, (ii) the current value and/orfuture value, or (iii) a product or service desired for customization oran offering. In an example embodiment of the invention, there may be afirst set of actions available for a particular customer segment, andthe eligibility rules associated with that first set of actions may beidentified. Similarly, the current value and/or future value, eitheralone or in some type of combination, may indicate a particular overallcustomer value that may be associated with a second set of actions, andthe eligibility rules associated with that second set of actions may beidentified. Finally, a preference or constraint may have been specifiedregarding the desirability of a product or service for customization oran offering, and the eligibility rules associated with a third set ofactions available for the product or service. It will be appreciatedthat the first, second, and third set of actions may each only include asingle action without departing from example embodiment of theinvention. In addition, the set of eligibility rules can also be basedon a combination of two or more of (i) the customer segment, (ii) thecurrent value and/or future value, (iii) a product or service desiredfor customization or an offering. It will be appreciated thateligibility rules may be based upon different factors than the threedescribed herein for illustrative purposes.

Accordingly, block 626 may identify a set of eligibility rules forrespective actions that may be provided for the customer. Block 628 mayinitialize by setting the Nth eligibility rule to be a next (e.g.,not-yet-processed) rule in the set of eligibility rules. If block 630determines that the end of the rules in the set have been reached, thenprocessing may stop. Otherwise, processing proceeds from block 630 toblock 632. Block 632 may determine whether the customer satisfies theNth eligibility rule. In general, the Nth eligibility rule will specifythe criteria that the customer must satisfy prior to being recommendedan action associated with the eligibility rule. As an example, a firstexample eligibility rule may be for a recommended action of a “PaymentHoliday”—that is, the customer may have the option of skipping a nextpayment on a loan product. The first example eligibility rule for thisrecommended action may require that the customer have made apredetermined number of consecutive on-time payments since any priorpayment holiday received. Likewise, the first example eligibility rulemay further specify that only certain types of loan products (e.g.,credit card, HELOC, auto loan, etc.) of the customer will be consideredfor a payment holiday. As another example, a second example eligibilityrule may be for a recommended action of a “fee waiver”. If the feewaiver is for a customer's credit card, the second example eligibilityrule for this recommended action may require that the customer havecharged at least a certain amount of purchases on the credit card over aperiod of time (e.g., for the past 6 months, year, etc.). Likewise, ifthe fee waiver is for a new product (i.e., a fee waiver for closingcosts on a HELOC) to be recommended to the customer, then theeligibility rule for this recommended action may require that thecustomer be within a particular segment (e.g., relationship agnostic)and/or have at least a threshold amount of liquid assets with thefinancial institution. It will be appreciated that other additionaleligibility rules may be provided, including one or more of thefollowing in Table V.

TABLE V Associated Eligibility Rule Recommended Action Customercurrently not enrolled in online Recommend Online banking BankingCustomer not registered for electronic bill Recommend electronic paymentservices bill payment services Customer current receiving papernotifications Recommend notifications via email

If the Nth eligibility rule at block 632 is not satisfied, thenprocessing may proceed to block 634, where the Nth recommended actionmay be set to “None” to indicate that no action is recommended. On theother hand, if the Nth eligibility rule at block 632 is satisfied, thenprocessing may proceed to block 636. Block 636 may determine whether anNth recommended action may be duplicative. The duplication may be in oneof at least two situations. First, the customer may already have aproduct/service that may be recommended by the Nth recommend action.Second, a prior recommended action may have likewise provided the sameaction as the Nth recommended action, according to an example embodimentof the invention. If the action would not be duplicative, thenprocessing may proceed to block 638. At block 638, the Nth recommendedaction may be set in accordance with the Nth eligibility rule. Followingblock 638 or block 634, processing may return to block 628. Processingmay end when block 630 determines that the end of the eligibility rulesin the set has been each set. It will be appreciated that therecommended actions for the customer may be stored in association with acustomer identifier in database 170 and/or data files 172, according toan example embodiment of the invention.

Following the process of FIG. 6, one or more eligible products/servicesmay be selected for recommendation to the customer. For example, one ormore constraints or preferences may have been previously set such thatonly a maximum number of products/services may be recommended for anyparticular customer. Accordingly, if the customer is eligible for morethan the maximum number of products/services, then a prioritizationscheme may be used to determine which products/services are availablefor recommendation to the customer. For example, the prioritization maybe based upon, for example, least costly to most costly cost ofacquisition for a product/service, or alternatively or additionally, onmost profitable to least profitable product/service. The one or morerecommended products/services for the customer may be stored inassociation with a customer identification in database 170 or data files172, according to an example embodiment of the invention.

FIG. 8 illustrates an example implementation of block 340 that isdirected towards revenue and/or cost improvement optimization, accordingto an example embodiment of the invention. It will be appreciated thatFIG. 8 may be utilized where an optimization objection objective is todetermine what actions(s) (e.g., promotions, offerings ofproducts/services, fee structures, configurations of products/services),if any, should be taken to improve revenue associated with the customeror otherwise reduce costs associated with a particular customer. Turningnow to FIG. 8, at block 802, the customer under consideration can beidentified. In conjunction with identifying the customer, block 802 mayfurther identify or retrieve one or more previously calculated modelingscores and/or computational values (e.g., attrition risk, etc.)determined at block 215. In addition, any other input data, for exampletransaction and/or non-transactional data of the customer, that may beneeded for performing the product/service origination optimization mayalso be identified or retrieved at block 802.

At block 804, the segment associated with the customer may likewise beidentified. It will be appreciated that the segment may have beenpreviously determined for the customer at block 210. Following block 804is block 806. At block 806, the attrition risk associated with thecustomer may be compared to a threshold value to determine whether theattrition risk is acceptable. For example, the attrition risk may beacceptable if it is less than a threshold value, and unacceptable if itis greater than the threshold, or vice-versa. The threshold value forattrition risk may be set to determine which customers should beconsidered for one or more products or services in accordance with anexample revenue and/or cost improvement optimization. The thresholdvalue may be static in one embodiment. However, in another embodiment,the threshold value may be dynamic, for example, when obtained or set byone or more constraints or preferences at block 310 (or similarly, block220).

If the attrition risk is not acceptable at block 806, then processingmay proceed to block 807, where no action may be recommended for thecustomer. For example, no action may be recommended for a customer wherethere is a high likelihood of losing the customer in the next X days. Onthe other hand, the attrition risk may be acceptable at block 806 andprocessing may proceed to block 808. At block 808, the customer segmentmay be utilized at least in part to determine the list of availableproducts or services for possible recommendation. For example, at leasta portion of the customers in first segment (e.g., branch churners) maybe considered for the following list of available products or services:(i) debit card, (ii) debit rewards for signature-based cardtransactions, (iii) an ATM deposit incentive (e.g., an entry in apromotional giveaway), (iv) an online banking incentive (e.g., $X bonusfor your first online banking bill payment), or (v) fee adjustment(increase or decrease for one or more products or services). Likewise,at least a portion of the customers in the second segment (e.g., youngdigerati) may be considered for the following list of available productsor services: (i) home equity line of credit with an access provision foronline banking transfers to a checking or money market account, (ii) a“package” of rewards services tied to a checking account that can beredeemed for online purchases, (iii) “no holds” on deposits made to ATM.

Following block 808 is 810. At block 810, the list of available productsor services generated at block 808 may be updated to remove thoseproducts or services already utilized by the customer. The removal ofduplicative products or services from the list prevents the customerfrom being offered a product or service that the customer already ownsor utilizes, according to an example embodiment of the invention.Following block 810, processing may proceed to block 812. At block 812,if the updated list is empty, then processing may proceed to block 807,where no action may be recommended for the customer. On the other hand,at block 812, if the updated list is not empty, then processing mayproceed to block 814.

Block 814 may set the Nth product/service to be a next (e.g.,not-yet-processed) product/service in the updated list. Processing mayproceed from block 814 to block 816, where it is determined whether theend of the updated list has been reached. If the end of the list has notbeen reached at block 816, then processing may proceed to block 820.Block 820 may determine whether the eligibility requirement(s)/criteriafor the Nth product/service have been satisfied. If block 820 has beensatisfied, then the Nth product/service eligibility is set to “yes”; inother words, the customer is eligible for the Nth product/service,according to an example embodiment of the invention. On the other hand,if block 820 has not been satisfied, then the Nth product/serviceeligibility is set to “no”; in other words, the customer is not eligiblefor the Nth product/service. It will be appreciated that there may be avariety of requirements/criteria utilized for block 820. As an example,Table VI below illustrates a few eligibility requirements/criteria forexample products/services.

TABLE VI Product/Service Eligibility Requirement(s)/Criteria Debit CardOffering Customer currently does not have debit card Debit cardactivation Customer has a debit card but has not activated it Debit cardRewards Customer has a debit card, has activated, but has not used cardin past X days or months Debit Card Purchase Customer makes purchases ondebit card, but Incentives only for certain categories (gas only/gas andgroceries/Get cash from ATMs only) Debit Rewards for Customer has DebitCard and primarily uses Signature-based Card PIN-based card transactions(through Transactions Electronic Funds Transfers (EFT) network) ATMDeposit Incentive Customer has made teller-based deposit, but no ATMDeposits within the past X months or does not have an ATM or Debit cardOnline Banking Incentive Customer not registered or enrolled for onlinebanking Fee Based Account Customer has low attrition risk and lowcurrent value and/or future value Online Bill Pay Customer uses onlinebanking but not electronic bill payment Credit Line activation Customerhas a revolving line of credit that has incentive not been activated

Following block 824 or block 822, processing may return to block 814,where the next product/service in the updated list may be selected. Whenblock 816 determines that the end of the updated list has been reached,processing may proceed to block 818. At block 818, one or more of theeligible products/services may be selected for recommendation. Forexample, one or more constraints or preferences may have been previouslyset such that only a maximum number of products/services may berecommended for any particular customer. Accordingly, if the customer iseligible for more than the maximum number of products/services, then aprioritization scheme may be used to determine which products/servicesare available for recommendation to the customer. For example, theprioritization may be based upon, for example, least costly to mostcostly cost of acquisition for a product/service, or alternatively oradditionally, on most profitable to least profitable product/service.The one or more recommended products/services for the customer may bestored in association with a customer identification in database 170 ordata files 172, according to an example embodiment of the invention.

It will be appreciated that the results of performing one or moreoptimization processes such as those described for blocks 320, 330, 340,may be stored one or more recommendations for one or more customers,according to an example embodiment of the invention. The recommendationsmake take the form of offerings of new products/services, modificationsto existing products/services, and/or configurations of both new andexisting products/services. The recommendations may be stored indatabase 170 and/or data files 180, perhaps in conjunction with acustomer identifier to facilitate later retrieval of any recommendationsfor particular customers. The recommendations may be provided forutilization in many different ways and formats. For example, theoptimization computer 160 may operate as an application service provider(ASP) such that the one or more recommendations for one or morecustomers can be available for retrieval by a financial institutioncomputer 140. Indeed, a financial institution computer 140 may beoperated by a teller at a financial institution, a customer servicerepresentative at a call center associated with the financialinstitution, an automated teller machine (ATM), or any other employee,contractor, or entity associated with the financial institution. Theoptimization computer 160 can also push or deliver the one or morerecommendations for one or more customers to a financial institutioncomputer 140. (or other computer), which can then store therecommendations with its own financial institution data and use it inthe context of a variety of financial institution applications (e.g.,data mining, campaign management, teller interfaces, customer careinterfaces, online banking interacting directly with the bank customer,etc.). On the other hand, an optimization computer 160 can also run theoptimization processes described herein such that the recommendationsgenerated from the optimizations can be available locally at theoptimization computer 160. However, the optimization computer 160 mayalso function as an application service provider for any number ofinternal or external business units, subsidiaries, or affiliates,including other financial institutions, according to an exampleembodiment of the invention.

In some example embodiments, the recommendations may already specify achannel for contacting the customer in conjunction with therecommendation. However, in other example embodiments, the channel mayneed to be determined for a particular customer. It will be appreciatedthat many channels may be available, which may be electronic ornon-electronic channels. Example channels can include paper mailings,facsimile, email, text message, instant message, Internet presentation(e.g., via online banking website, mobile application, etc.), or aninteractive voice response system (IVR). Other channels can includein-person communications with a teller or other representative of afinancial institution, a phone call with a customer servicerepresentative, video conference, and the like. The channel can bedetermined in one of many ways, including, but not limited to one ormore of the following:

-   -   Based upon the prior channels utilized by the customer: A        channel can be selected by identifying one of (i) the channel        utilized most often (e.g., a majority) by the customer over a        period of time; or (ii) the most recent channel utilized by the        customer.    -   Based upon a segment of the customer: The customer segment may        be indicative of whether customers in a particular segment are        likely to have certain channel preferences. For example,        customers in a branch-centric segment, may prefer to communicate        directly (e.g., in-person) with a teller or employee at a        financial institution, or otherwise receive paper mailings. On        the other hand, customers in a more technologically savvy        segment, may prefer to receive electronic communications, such        as communications through email, text message, webpage        presentation, mobile application, automated teller machine, etc.    -   Multi-channel: Where a customer may visit branches, conduct        online banking, use telephone banking and use ATMs together. In        this case, the multiple channels can be supported.    -   Based upon preferences specified by an individual customer: The        customer may have previously specified one or more preferred        channels for receiving one or more types of communications        (e.g., statements, bills, inquiries, new product/service        offerings, etc.).

The channel for contacting the customer in conjunction with therecommendation may determine how the recommendation is utilized byfinancial institution computer 140 and/or optimization computer 160. Forexample, if the customer will be offered a product/service via papermailing, then the financial institution computer 140 and/or optimizationcomputer 160 may deliver the recommendation with customer identificationinformation (e.g., name, mailing address) to a printing service togenerate and mail the paper mailings to the customers. On the otherhand, if the customer will be offered a product/service via email, textmessage, Internet presentation, facsimile, ATM, or other electronicchannels not requiring additional interaction with a person, then thefinancial institution computer 140 and/or optimization computer 160 maydeliver the recommendation with customer identification information tothe appropriate electronic systems as necessary to effectuate thedelivery. Example customer identification information for the followingchannels may include, but are not limited to:

-   -   Email: Customer's name, email address    -   Text message: Customer's name, mobile telephone number    -   Internet presentation: Customer's name, online banking account        identification    -   Facsimile: Customer's name, fax number    -   Automated Teller Machine: Customer's name, bank account number,        debit card number, etc.    -   Social Network Presentation: Customer's social network addresses        or membership number (e.g., for Facebook, Twitter, etc.).

As an example, email, text message, and facsimile communications may beelectronically pushed by an appropriate server to the destinationindicated by the customer identification (e.g., email address, mobilephone number, fax number, etc.). In this regard, the recommendations canbe pushed to the customers with information regarding the recommendedproduct/service. Likewise, one or more of the recommendations caninclude a hyperlink or other Internet address for enrollment orregistration in the product/service, or otherwise indicating whereadditional information can be obtained for the recommended product orservice, according to an example embodiment of the invention. Forrecommendations by Internet presentation or Automated Teller Machine(ATM), the recommendations may be stored for ready access such thatrecommendations are presented when the customer accesses online bankingfunctionality through an Internet website, a mobile financialapplication, or otherwise accesses functionality of an ATM.

The operations described and shown with reference to the above methodsmay be carried out or performed in any suitable order as desired invarious embodiments of the invention. Additionally, in certainembodiments, at least a portion of the operations may be carried out inparallel. Furthermore, in certain embodiments, less than or more thanthe operations described herein may be performed.

The invention is described above with reference to block and flowdiagrams of systems, methods, apparatus, and/or computer programproducts according to example embodiments of the invention. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, or may not necessarily need to be performed at all, accordingto some embodiments of the invention.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. A special-purpose computer may be a general-purposecomputer that is programmed to perform one or more of the stepsdiscussed herein. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks. As an example, embodiments of the invention may provide for acomputer program product, comprising a computer usable medium having acomputer-readable program code or program instructions embodied therein,said computer-readable program code adapted to be executed to implementone or more functions specified in the flow diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational elements or steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide elements or steps for implementing the functionsspecified in the flow diagram block or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specifiedfunctions, and program instruction means for performing the specifiedfunctions. It will also be understood that each block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains andhaving the benefit of the teachings presented in the foregoingdescriptions and the associated drawings. Therefore, it is to beunderstood that the invention is not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1-24. (canceled)
 25. A system, comprising: at least one networkinterface; at least one memory storing computer-executable instructions;and at least one processor communicatively coupled to the at least onenetwork interface and the at least one memory, and configured to accessthe at least one memory and execute the computer-executable instructionsto: receive, via at least a first network interface of the at least onenetwork interface, and on behalf of a financial institution, i) anindication of one or more optimization objectives associated with one ormore optimization processes and ii) an indication of one or moreoptimization constraints associated with the one or more optimizationprocesses; receive, via at least a second network interface of the atleast one network interface, customer financial data associated with acustomer of the financial institution, wherein the customer financialdata comprises at least one of i) financial account data associated withthe customer or ii) financial transaction data associated with thecustomer; generate derived data associated with the customer based atleast in part on the customer financial data, wherein the derived datacomprises at least one of i) data indicative of a customer segment withwhich the customer is associated, ii) data indicative of one or morepredictive model values generated based at least in part on one or morepredictive models, or iii) data indicative of one or more computationalvalues; determine that the customer is eligible for at least one of theone or more optimization processes based at least in part on i) at leasta portion of the derived data and ii) the one or more optimizationconstraints; identify, based at least in part on the one or moreoptimization objectives, a particular optimization process to executefor the customer, wherein the at least one of the one or moreoptimization processes comprises the particular optimization process;execute the particular optimization process to identify a recommendednext action to be taken with respect to the customer; and transmit, viaat least a third network interface of the at least one networkinterface, information indicative of the recommended next action. 26.The system of claim 25, wherein the particular optimization processcomprises one of: i) a product/service origination optimization process,ii) a relationship optimization process, or iii) a revenue/costimprovement optimization process.
 27. The system of claim 26, whereinthe particular optimization process comprises the product/serviceorigination optimization process, wherein the derived data comprises thedata indicative of one or more predictive model values generated basedat least in part on one or more predictive models and the dataindicative of one or more computational values, wherein the one or morepredictive model values comprise a respective probability of purchasefor each candidate product or service of a set of one or more candidateproducts or services, and wherein the at least one processor isconfigured to execute the product/service origination optimizationprocess by executing the computer-executable instructions to: identify asubset of the set of one or more candidate products or services, whereinthe identifying comprises determining that the respective probability ofpurchase associated with each candidate product or service of the subsetmeets or exceeds a threshold value; and identify a particular candidateproduct or service of the subset based at least in part on a respectivefuture product or service value associated with the particular candidateproduct or service and included in the one or more computational values,wherein the recommended next action comprises an offering of theparticular candidate product or service.
 28. The system of claim 27,wherein the at least one processor is further configured to execute theproduct/service optimization process by executing thecomputer-executable instructions to: generate a customized offering ofthe particular candidate product or service, wherein the recommendednext action comprises the customized offering of the particularcandidate product or service.
 29. The system of claim 27, wherein thethreshold value is a first threshold value, the subset is a firstsubset, and the at least one processor is further configured to executethe product/service origination optimization process by executing thecomputer-executable instructions to: determine a respective futureproduct or service value associated with each of at least one candidateproduct or service of the first subset; determine a future customervalue associated with the customer based at least in part on therespective future product or service value associated with the each ofthe at least one candidate product or service of the first subset;determine that the future customer value meets or exceeds a secondthreshold value; and identify a second subset of one or more candidateproducts or services from the first subset, wherein the respectivefuture product or service value associated with each candidate productor service of the second subset is greater than the respective futureproduct or service value associated with each candidate product orservice of the first subset that does not form part of the secondsubset, wherein the particular candidate product or service is includedin the second subset.
 30. The system of claim 29, wherein the at leastone processor is further configured to execute the product/serviceorigination optimization process by executing the computer-executableinstructions to: identify a third subset of one or more candidateproducts or services from the second subset, wherein each candidateproduct or service in the third subset is not currently held by thecustomer; identify a candidate product or service of the third subsetthat is associated with a greater respective future product or servicevalue than each other candidate product or service in the third subset;and select the identified candidate product or service of the thirdsubset as the particular candidate product or service.
 31. The system ofclaim 27, wherein the threshold value is a first threshold value, andwherein at least one processor is configured to identify the particularcandidate product or service of the subset by executing thecomputer-executable instructions to determine that the respective futureproduct or service value associated with the particular candidateproduct or service meets or exceeds a second threshold value.
 32. Thesystem of claim 26, wherein the particular optimization processcomprises the relationship optimization process, and wherein the atleast one processor is configured to execute the relationshipoptimization process by executing the computer-executable instructionsto: determine a current customer value associated with the customer;determine a future customer value associated with the customer;determine an overall customer value associated with the customer basedat least in part on a combination of the current customer value and thefuture customer value; and determine a value level associated with thecustomer based at least in part on at least one of: (i) a comparison ofthe overall value associated with the customer to one or more overallvalue thresholds or (ii) a comparison of the future customer value to afirst threshold and a comparison of the current customer value to asecond threshold, wherein the recommended next action is determinedbased at least in part on the value level associated with the customer.33. The system of claim 32, wherein the at least one processor isfurther configured to execute the relationship optimization process byexecuting the computer-executable instructions to: identify a respectiveset of one or more eligibility rules associated with each of one or morecandidate recommended next actions; analyze the respective set of one ormore eligibility rules associated with each of at least one of the oneor more candidate recommended next actions; determine, based at least inpart on the analyzing, that each eligibility rule in the respective setof one or more eligibility rules associated with a particular candidaterecommended next action is satisfied; and select the particularcandidate recommended next action as the recommended next action. 34.The system of claim 33, wherein the at least one processor is configuredto identify the respective set of one or more eligibility rulesassociated with each of the one or more candidate recommended nextactions based at least in part on at least one of: (i) the customersegment associated with the customer, (ii) the current customer value,(iii) the future customer value, (iv) the value level associated withthe customer, or (v) the one or more optimization constraints.
 35. Thesystem of claim 26, wherein the at least one processor is configured todetermine the current customer value by executing thecomputer-executable instructions to: identify a set of one or moreproducts or services currently held by the customer; determine arespective set of one or more financial metrics associated with eachproduct or service in the set of one or more products or services;determine a respective current product or service value for each productor service in the set of one or more products or services based at leastin part on the respective set of one or more financial metrics; anddetermine the current customer value based at least in part on acombination of each respective current product or service value.
 36. Thesystem of claim 26, wherein the particular optimization process is arevenue/cost improvement optimization process, wherein the derived datacomprises the data indicative of a customer segment with which thecustomer is associated and the data indicative of one or more predictivemodel values, wherein the one or more predictive model values comprisean attrition risk associated with the customer, and wherein the at leastone processor is configured to execute the revenue/cost improvementoptimization process by executing the computer-executable instructionsto: determine that the attrition risk meets or exceeds a thresholdvalue; determine a set of one or more candidate products or servicesbased at least in part on the customer segment; identify a subset of oneor more candidate products or services from the set of one or morecandidate products or services, wherein the one or more candidateproducts or services of the subset are not currently held by thecustomer; identify a respective set of one or more eligibility rulesassociated with each candidate product or service in the subset; analyzethe respective set of one or more eligibility rules associated with eachcandidate product or service in the subset; and determine, based atleast in part on the analyzing, that each eligibility rule in therespective set of one or more eligibility rules associated with aparticular candidate product or service is satisfied, wherein therecommended next action is an offering of the particular candidateproduct or service.
 37. The system of claim 25, wherein the one or moreoptimization objectives comprise at least one of: i) identification of acandidate product or service to offer to the customer, ii) determinationas to whether the customer is eligible for a particular product orservice, or iii) determination of a set of one or more actions forimproving a relationship between the customer and the financialinstitution.
 38. The system of claim 25, wherein the one or moreoptimization constraints comprise at least one of: i) restricting therecommended next action to an offering to the customer of a product orservice included in a predetermined set of one or more products orservices, ii) limiting a cost of acquisition associated with therecommended next action to a first maximum threshold value, iii)requiring a revenue increase or cost decrease associated with therecommended next action to meet or exceed a minimum threshold value, iv)restricting targeting of the recommended next action to a predeterminedset of one or more customer segments comprising the customer segmentwith which the customer is associated; v) restricting transmission ofinformation associated with the recommended next action to apredetermined set of one or more channels; or vi) limiting a risk ofdefault or delinquency to a second maximum threshold value.
 39. Thesystem of claim 25, wherein at least one of the first network interface,the second network interface, or the third network interface are a samenetwork interface.
 40. A method, comprising: receiving, by acomputerized financial system comprising one or more computers and onbehalf of a financial institution, i) an indication of one or moreoptimization objectives associated with one or more optimizationprocesses and ii) an indication of one or more optimization constraintsassociated with the one or more optimization processes; receiving, bythe computerized financial system, customer financial data associatedwith a customer of the financial institution, wherein the customerfinancial data comprises at least one of i) financial account dataassociated with the customer or ii) financial transaction dataassociated with the customer; generating, by the computerized financialsystem, derived data associated with the customer based at least in parton the customer financial data, wherein the derived data comprises atleast one of i) data indicative of a customer segment with which thecustomer is associated, ii) data indicative of one or more predictivemodel values generated based at least in part on one or more predictivemodels, or iii) data indicative of one or more computational values;determining, by the computerized financial system, that the customer iseligible for at least one of the one or more optimization processesbased at least in part on i) at least a portion of the derived data andii) the one or more optimization constraints; identifying, by thecomputerized financial system and based at least in part on the one ormore optimization objectives, a particular optimization process toexecute for the customer, wherein the at least one of the one or moreoptimization processes comprises the particular optimization process;executing, by the computerized financial system, the particularoptimization process to identify a recommended next action to be takenwith respect to the customer; and transmitting, by the computerizedfinancial system, information indicative of the recommended next action.41. The method of claim 40, wherein the particular optimization processcomprises one of: i) a product/service origination optimization process,ii) a relationship optimization process, or iii) a revenue/costimprovement optimization process.
 42. The method of claim 41, whereinthe particular optimization process comprises the product/serviceorigination optimization process, wherein the derived data comprises thedata indicative of one or more predictive model values generated basedat least in part on one or more predictive models and the dataindicative of one or more computational values, wherein the one or morepredictive model values comprise a respective probability of purchasefor each candidate product or service of a set of one or more candidateproducts or services, and wherein the product/service originationoptimization process comprises: identifying, by the computerizedfinancial system, a subset of the set of one or more candidate productsor services, wherein the identifying comprises determining that therespective probability of purchase associated with each candidateproduct or service of the subset meets or exceeds a threshold value;identifying, by the computerized financial system, a particularcandidate product or service of the subset based at least in part on arespective future product or service value associated with theparticular candidate product or service and included in the one or morecomputational values, wherein the recommended next action comprises anoffering of the particular candidate product or service.
 43. The methodof claim 42, wherein the threshold value is a first threshold value, thesubset is a first subset, and the product/service originationoptimization process further comprises: determining, by the computerizedfinancial system, a respective future product or service valueassociated with each of at least one candidate product or service of thefirst subset; determining, by the computerized financial system, afuture customer value associated with the customer based at least inpart on the respective future product or service value associated withthe each of the at least one candidate product or service of the firstsubset; determining, by the computerized financial system, that thefuture customer value meets or exceeds a second threshold value; andidentifying, by the computerized financial system, a second subset ofone or more candidate products or services from the first subset,wherein the respective future product or service value associated witheach candidate product or service of the second subset is greater thanthe respective future product or service value associated with eachcandidate product or service of the first subset that does not form partof the second subset, wherein the particular candidate product orservice is included in the second subset.
 44. The method of claim 43,wherein the product/service origination optimization process furthercomprises: identifying, by the computerized financial system, a thirdsubset of one or more candidate products or services from the secondsubset, wherein each candidate product or service in the third subset isnot currently held by the customer; identifying, by the computerizedfinancial system, a candidate product or service of the third subsetthat is associated with a greater respective future product or servicevalue than each other candidate product or service in the third subset;and selecting, by the computerized financial system, the identifiedcandidate product or service of the third subset as the particularcandidate product or service.
 45. The method of claim 42, wherein thethreshold value is a first threshold value, and wherein identifying theparticular candidate product or service of the subset that therespective future product or service value associated with theparticular candidate product or service meets or exceeds a secondthreshold value.
 46. The method of claim 40, wherein the particularoptimization process comprises the relationship optimization process,and wherein the relationship optimization process comprises:determining, by the computerized financial system, a current customervalue associated with the customer; determining, by the computerizedfinancial system, a future customer value associated with the customer;determining, by the computerized financial system, an overall customervalue associated with the customer based at least in part on acombination of the current customer value and the future customer value;and determining, by the computerized financial system, a value levelassociated with the customer based at least in part on at least one of:(i) a comparison of the overall value associated with the customer toone or more overall value thresholds or (ii) a comparison of the futurecustomer value to a first threshold and a comparison of the currentcustomer value to a second threshold, wherein the recommended nextaction is determined based at least in part on the value levelassociated with the customer.
 47. The method of claim 46, wherein therelationship optimization process further comprises: identifying, by thecomputerized financial system, a respective set of one or moreeligibility rules associated with each of one or more candidaterecommended next actions; analyzing, by the computerized financialsystem, the respective set of one or more eligibility rules associatedwith each of at least one of the one or more candidate recommended nextactions; determining, by the computerized financial system and based atleast in part on the analyzing, that each eligibility rule in therespective set of one or more eligibility rules associated with aparticular candidate recommended next action is satisfied; andselecting, by the computerized financial system, the particularcandidate recommended next action as the recommended next action. 48.The method of claim 41, wherein the particular optimization process is arevenue/cost improvement optimization process, wherein the derived datacomprises the data indicative of a customer segment with which thecustomer is associated and the data indicative of one or more predictivemodel values, wherein the one or more predictive model values comprisean attrition risk associated with the customer, and wherein therevenue/cost improvement optimization process comprises: determining, bythe computerized financial system, that the attrition risk meets orexceeds a threshold value; determining, by the computerized financialsystem, a set of one or more candidate products or services based atleast in part on the customer segment; identifying, by the computerizedfinancial system, a subset of one or more candidate products or servicesfrom the set of one or more candidate products or services, wherein theone or more candidate products or services of the subset are notcurrently held by the customer; identifying, by the computerizedfinancial system, a respective set of one or more eligibility rulesassociated with each candidate product or service in the subset;analyzing, by the computerized financial system, the respective set ofone or more eligibility rules associated with each candidate product orservice in the subset; and determining, by the computerized financialsystem and based at least in part on the analyzing, that eacheligibility rule in the respective set of one or more eligibility rulesassociated with a particular candidate product or service is satisfied,wherein the recommended next action is an offering of the particularcandidate product or service.